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This paper investigates constant-stress accelerated life tests interrupted by a progressive type-I interval censoring regime. We provide a model based on the Chen distribution with a constant shape parameter and a log-linear connection between the scale parameter and stress loading. Inferential methods, whether classical or Bayesian, are employed to address model parameters and reliability attributes. Classical methods involve the estimation of model parameters through maximum likelihood and midpoint techniques. Bayesian approximations are achieved via the utilization of the Metropolis–Hastings algorithm, Tierney-Kadane procedure, and importance sampling methods. Furthermore, we engage in a discourse on the estimation of confidence intervals, making references to both asymptotic confidence intervals and credible intervals. To conclude, we furnish a simulation study, a corresponding discussion, and supplement these with an analysis of real data. [ABSTRACT FROM AUTHOR]

Past research indicates interviewer effects lead to an underestimation of network size and higher nonresponse to the "important matters" name generator. Self-administered surveys offer a potential solution, but evidence is mixed and context-specific. We employ a logistic multilevel regression, estimated using a Bayesian Markov Chain Monte Carlo approach, to analyze nonresponse to this name generator from 33 post-electoral surveys across 21 countries in the Comparative National Election Project. We find higher nonresponse in interviewer-administered surveys compared to self-administered surveys, particularly among specific demographic groups. Finally, we discuss the trade-offs in selecting survey modes for collecting ego-network data using this instrument. • Cross-country analysis of interview modes and nonresponse to the name generator. • Positive association between interviewer-administered surveys and nonresponse. • Consistency with prior studies on survey modes and nonresponse behavior. • Exploration of interplay between interviewer presence and respondent characteristics. • Caution advised in interpreting nonresponse as a sole indicator of social isolation. [ABSTRACT FROM AUTHOR]

Reproductive performance is a key determinant of cow longevity in a pasture-based, seasonal dairy system. Unfortunately, direct fertility phenotypes such as intercalving interval or pregnancy rate tend to have low heritabilities and occur relatively late in an animal's life. In contrast, age at puberty (AGEP) is a moderately heritable, early-in-life trait that may be estimated using an animal's age at first measured elevation in blood plasma progesterone (AGEP4) concentrations. Understanding the genetic architecture of AGEP4 in addition to genetic relationships between AGEP4 and fertility traits in lactating cows is important, as is its relationship with body size in the growing animal. Thus, the objectives of this research were 3-fold. First, to estimate the genetic and phenotypic (co)variances between AGEP4 and subsequent fertility during first and second lactations. Second, to quantify the associations between AGEP4 and height, length, and BW measured when animals were approximately 11 mo old (standard deviation = 0.5). Third, to identify genomic regions that are likely to be associated with variation in AGEP4. We measured AGEP4, height, length, and BW in approximately 5,000 Holstein-Friesian or Holstein-Friesian × Jersey crossbred yearling heifers across 54 pasture-based herds managed in seasonal calving farm systems. We also obtained calving rate (CR42, success or failure to calve within the first 42 d of the seasonal calving period), breeding rate (PB21, success or failure to be presented for breeding within the first 21 d of the seasonal breeding period) and pregnancy rate (PR42, success or failure to become pregnant within the first 42 d of the seasonal breeding period) phenotypes from their first and second lactations. The animals were genotyped using the Weatherby's Versa 50K SNP array (Illumina, San Diego, CA). The estimated heritabilities of AGEP4, height, length, and BW were 0.34 (90% credibility interval [CRI]: 0.30, 0.37), 0.28 (90% CRI: 0.25, 0.31), 0.21 (90% CRI: 0.18, 0.23), and 0.33 (90% CRI: 0.30, 0.36), respectively. In contrast, the heritabilities of CR42, PB21 and PR42 were all <0.05 in both first and second lactations. The genetic correlations between AGEP4 and these fertility traits were generally moderate, ranging from 0.11 to 0.60, whereas genetic correlations between AGEP4 and yearling body-conformation traits ranged from 0.02 to 0.28. Our GWAS highlighted a genomic window on chromosome 5 that was strongly associated with variation in AGEP4. We also identified 4 regions, located on chromosomes 14, 6, 1, and 11 (in order of decreasing importance), that exhibited suggestive associations with AGEP4. Our results show that AGEP4 is a reasonable predictor of estimated breeding values for fertility traits in lactating cows. Although the GWAS provided insights into genetic mechanisms underpinning AGEP4, further work is required to test genomic predictions of fertility that use this information. [ABSTRACT FROM AUTHOR]

• Bayesian multi-trait multi-environment model were used for sorghum breeding. • Bayesian genetic correlation among traits differed across tested sites. • This model might be successfully used in the genetic selection of sorghum genotypes. Grain sorghum is one of the most important cereals and is grown in arid and semi-arid regions due its high photosynthetic efficiency and tolerance to water deficit. Genotype-by-environment interactions and multi-trait selection are the main challenges in grain sorghum breeding. The objective of this study was to compare statistical methods for genetic evaluation and selection of parents and hybrids of grain sorghum. To this end, we conducted two trials and measured flowering, plant height and grain yield traits. Bayesian and frequentist multi-trait multi-environment (MTME) models were fitted through Markov Chain Monte Carlo (MCMC) and Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP), respectively. The Additive Index was used to perform the multi-trait selection. The Bayesian inference is more flexible, and our results suggest that the Bayesian MTME model outperform the frequentist MTME model and should be preferred for genetic evaluation. The Additive Index can be used for genetic selection of parents and hybrids of grain sorghum. [ABSTRACT FROM AUTHOR]

Sunscreen products constitute two distinct categories. Recreational sunscreens protect against high-intensity, episodic sun exposure, often applied over the entire body. In contrast, facial sunscreen products are designed for sub-erythemal, low-intensity daily sun exposure. Such different exposures necessitate distinctive product safety assessments. Building on earlier methods for predicting dermal disposition, a mechanistic model was developed to simulate plasma concentrations of seven organic sunscreen active ingredients: avobenzone, ensulizole, homosalate, octinoxate, octisalate, octocrylene, and oxybenzone, following facial application. In vitro permeation testing (IVPT) was performed with two different vehicles using a subset of the UV filters. These IVPT results, in addition to previously published IVPT data and published in vivo Maximal Usage Trial (MUsT) data for the UV filters, were used to train the mechanistic dermal model via a Bayesian Markov chain Monte Carlo (MCMC) method. An external validation of the trained model with real-world in vivo datasets demonstrated that the model's predicted UV filter plasma concentrations align well with experimental measurements and capture the observed inter-individual variability. Predictions of steady-state UV filter plasma concentrations under facial application scenarios at 5% concentration and at the maximal allowable concentrations were then generated by the trained model. Oxybenzone had the greatest predicted plasma concentration following facial application. Homosalate and octisalate predictions had high uncertainty associated with the absence of data. Several application scenarios pertaining to avobenzone, ensulizole, octocrylene and octinoxate were identified in which median plasma concentration levels were at 0.5 ng/ml or below when applied in the recreational or facial product. Model limitations include uncertainty in vehicle/water partitioning, formulation metamorphosis, and UV filter systemic clearance, all of which can be refined with additional data. For UV filters, limiting exposure to facial application reduces human safety concerns based on FDA established thresholds. [ABSTRACT FROM AUTHOR]

Reliability estimation is paramount to predicting costs, planning maintenance actions, and estimating system availability in high-risk industries such as aerospace, aeronautics, and Oil and Gas (O&G). However, a shortage of historical data is very common because of restrictions related to manufacturer rights, costs, or collecting the data. In this context, generic datasets serve as prior information to model failure distributions. A popular approach to dealing with non-homogeneous generic data assumes that the failure rate is constant, which is not true in many cases. In this paper, we propose a Bayesian approach to consider non-constant failure rates using the Weibull distribution. Here, we account for the uncertainty on the Weibull parameters by modeling the population variability via Empirical Bayes. In addition, the challenge is to consider the generic dataset to provide interval-censored failure data. To obtain the posterior distribution after incorporating specific data on the equipment of interest, the Bayesian model also considers the likelihood of complete failure and censored data. As there is no family of conjugate distributions, the posterior estimate is sampled through the Markov Chain Monte Carlo method. The model was tested using simulated data as an application example. [ABSTRACT FROM AUTHOR]

Natural gas pipeline leakage can cause significant economic losses and even jeopardize public safety. Therefore, leakage detection technology is crucial. In this study, the pipeline source term estimation method is proposed to identify the source of natural gas pipeline leakage. The method combines the modified Gaussian plume model, the unmanned aerial vehicle inspection technology equipped with laser methane sensors, and the Markov chain Monte Carlo method based on Bayesian inference. Different from traditional leak detection techniques, this method can simultaneously obtain the location and rate of pipeline leakage. The accuracy of the method is validated by using simulated data from the modified Gaussian plume model, which can be employed to replace experimental tests. Convergence diagnosis and model evaluation are performed on the inversion model in this method, verifying its rationality and feasibility. The impact of system errors on the pipeline source estimation method is analyzed, revealing that the accuracy of inversion can be improved by using prior information and ensuring the reliability of the forward gas dispersion model and monitoring data. The pipeline source term estimation method proposed in this paper can offer technical support for the emergency treatment of natural gas pipeline leakage accidents. [ABSTRACT FROM AUTHOR]

This paper addresses a large class of nonsmooth nonconvex stochastic DC (difference-of-convex functions) programs where endogenous uncertainty is involved and i.i.d. (independent and identically distributed) samples are not available. Instead, we assume that it is only possible to access Markov chains whose sequences of distributions converge to the target distributions. This setting is legitimate as Markovian noise arises in many contexts including Bayesian inference, reinforcement learning, and stochastic optimization in high-dimensional or combinatorial spaces. We then design a stochastic algorithm named Markov chain stochastic DCA (MCSDCA) based on DCA (DC algorithm) - a well-known method for nonconvex optimization. We establish the convergence analysis in both asymptotic and nonasymptotic senses. The MCSDCA is then applied to deep learning via PDEs (partial differential equations) regularization, where two realizations of MCSDCA are constructed, namely MCSDCA-odLD and MCSDCA-udLD, based on overdamped and underdamped Langevin dynamics, respectively. Numerical experiments on time series prediction and image classification problems with a variety of neural network topologies show the merits of the proposed methods. • We study stochastic DC programs where i.i.d. samples are unavailable. • An algorithm named Markov chain stochastic DCA is proposed and analyzed. • Two realizations of the algorithm are designed in the context of deep learning. • We conduct numerical experiments on time series prediction and image classification. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The "constant speed prediction" is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control. • The economy and durability of vehicular fuel cell system are both considered. • Markov Chain Monte Carlo is imported into MPC theory for vehicular power prediction. • Adaptability to changing conditions is strengthened via "constant speed prediction". • The computational load in MPC optimization is reduced by simplifying DP algorithm. [ABSTRACT FROM AUTHOR]

Much of the theory of estimation for exponential family models, which include exponential-family random graph models (ERGMs) as a special case, is well-established and maximum likelihood estimates (MLEs) in particular enjoy many desirable properties. However, in the case of many ERGMs, direct calculation of MLEs is impossible and therefore methods for approximating MLEs and/or alternative estimation methods must be employed. Many MLE approximation algorithms require an alternative estimate as a starting point. The maximum pseudo-likelihood estimator (MPLE) is frequently taken as this starting point. Here, we discuss a potentially large class of such alternatives based on the fact that, unlike the MLE, the MPLE fails to satisfy the so-called "likelihood principle". This means that different networks may have different MPLEs even if they have the same sufficient statistics. We exploit this fact here to search for improved starting values for approximation-based MLE methods. The method we propose has shown its merit in producing an MLE for a network dataset and model that had defied estimation using all other known methods. • Improving MCMC starting values. • Using simulated annealing for finding starting value. • Maximum pseudolikelihood and simulated annealing. • MLE, MPLE, and the likelihood principle. • Exponential family model for networks. [ABSTRACT FROM AUTHOR]

The exponential-family random graph models (ERGMs) have emerged as an important framework for modeling social networks for a wide variety of relational types. ERGMs for valued networks are less well-developed than their unvalued counterparts, and pose particular computational challenges. Network data with edge values on the non-negative integers (count-valued networks) is an important such case, with examples ranging from the magnitude of migration and trade flows between places to the frequency of interactions and encounters between individuals. Here, we propose an efficient parallelizable subsampled maximum pseudo-likelihood estimation (MPLE) scheme for count-valued ERGMs, and compare its performance with existing Contrastive Divergence (CD) and Monte Carlo Maximum Likelihood Estimation (MCMLE) approaches via a simulation study based on migration flow networks in two U.S. states. Our results suggest that edge value variance is a key factor in method performance, while network size mainly influences their relative merits in computational time. For small-variance networks, all methods perform well in point estimations while CD greatly overestimates uncertainties, and MPLE underestimates them for dependence terms; all methods have fast estimation for small networks, but CD and subsampled multi-core MPLE provides speed advantages as network size increases. For large-variance networks, both MPLE and MCMLE offer high-quality estimates of coefficients and their uncertainty, but MPLE is significantly faster than MCMLE; MPLE is also a better seeding method for MCMLE than CD, as the latter makes MCMLE more prone to convergence failure. The study suggests that MCMLE and MPLE should be the default approach to estimate ERGMs for small-variance and large-variance valued networks, respectively. We also offer further suggestions regarding choice of computational method for valued ERGMs based on data structure, available computational resources and analytical goals. • Computation remains a hurdle for analysis of valued networks using ERGMs. • We implement a parallelizable subsampled Maximum Pseudo-likelihood Estimation method. • Edge value variance is a key factor in method performance. • Monte Carlo Maximum Likelihood Estimation excels when edge variance is small. • Maximum Pseudo-likelihood Estimation performs better when edge variance is large. [ABSTRACT FROM AUTHOR]

Uncertainty quantification (UQ) tasks, such as sensitivity analysis and parameter estimation, entail a huge computational complexity when dealing with input-output maps involving the solution of nonlinear differential problems, because of the need to query expensive numerical solvers repeatedly. Projection-based reduced order models (ROMs), such as the Galerkin-reduced basis (RB) method, have been extensively developed in the last decades to overcome the computational complexity of high fidelity full order models (FOMs), providing remarkable speed-ups when addressing UQ tasks related with parameterized differential problems. Nonetheless, constructing a projection-based ROM that can be efficiently queried usually requires extensive modifications to the original code, a task which is non-trivial for nonlinear problems, or even not possible at all when proprietary software is used. Non-intrusive ROMs – which rely on the FOM as a black box – have been recently developed to overcome this issue. In this work, we consider ROMs exploiting proper orthogonal decomposition to construct a reduced basis from a set of FOM snapshots, and Gaussian process regression (GPR) to approximate the RB projection coefficients. Two different approaches, namely a global GPR and a tensor-decomposition-based GPR, are explored on a set of 3D time-dependent solid mechanics examples. Finally, the non-intrusive ROM is exploited to perform global sensitivity analysis (relying on both screening and variance-based methods) and parameter estimation (through Markov chain Monte Carlo methods), showing remarkable computational speed-ups and very good accuracy compared to high-fidelity FOMs. [ABSTRACT FROM AUTHOR]

Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. Although several works have studied theoretical and numerical properties of sparse neural architectures, they have primarily focused on the edge selection. Sparsity through edge selection might be intuitively appealing; however, it does not necessarily reduce the structural complexity of a network. Instead pruning excessive nodes leads to a structurally sparse network with significant computational speedup during inference. To this end, we propose a Bayesian sparse solution using spike-and-slab Gaussian priors to allow for automatic node selection during training. The use of spike-and-slab prior alleviates the need of an ad-hoc thresholding rule for pruning. In addition, we adopt a variational Bayes approach to circumvent the computational challenges of traditional Markov Chain Monte Carlo (MCMC) implementation. In the context of node selection, we establish the fundamental result of variational posterior consistency together with the characterization of prior parameters. In contrast to the previous works, our theoretical development relaxes the assumptions of the equal number of nodes and uniform bounds on all network weights, thereby accommodating sparse networks with layer-dependent node structures or coefficient bounds. With a layer-wise characterization of prior inclusion probabilities, we discuss the optimal contraction rates of the variational posterior. We empirically demonstrate that our proposed approach outperforms the edge selection method in computational complexity with similar or better predictive performance. Our experimental evidence further substantiates that our theoretical work facilitates layer-wise optimal node recovery. [ABSTRACT FROM AUTHOR]

In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, the Markov Chain Generative Adversarial Neural Network (MCGAN), to alleviate the computational costs associated with solving the Bayesian inference problem. GANs pose a very suitable framework to aid in the solution of Bayesian inference problems, as they are designed to generate samples from complicated high-dimensional distributions. By training a GAN to sample from a low-dimensional latent space and then embedding it in a Markov Chain Monte Carlo method, we can highly efficiently sample from the posterior, by replacing both the high-dimensional prior and the expensive forward map. This comes at the cost of a potentially expensive offline stage in which training data must be simulated or gathered and the GAN has to be trained. We prove that the proposed methodology converges to the true posterior in the Wasserstein-1 distance and that sampling from the latent space is equivalent to sampling in the high-dimensional space in a weak sense. The method is showcased in two test cases where we perform both state and parameter estimation simultaneously and it is compared with two conventional approaches, polynomial chaos expansion and ensemble Kalman filter, and a deep learning-based approach, deep Bayesian inversion. The method is shown to be more accurate than alternative approaches while also being computationally faster, in multiple test cases, including the important engineering setting of detecting leaks in pipelines. [ABSTRACT FROM AUTHOR]

• Pose a state-space model to analyze the early phase of the COVID-19 pandemic in the 27 countries of the European Union. • Model explicitly the uncertainty in the dynamic model parameters, model adequacy, and observation process. • Include the behavior induced effective population effect into the dynamical model. • Carry out structural identifiability analysis. • Disentangle virus infectiousness and human behavior during the early phase of the COVID-19 pandemic in the European Union. In this work, we manage to disentangle the role of virus infectiousness and awareness-based human behavior in the COVID-19 pandemic. Using Bayesian inference, we quantify the uncertainty of a state-space model whose propagator is based on an unusual SEIR-type model since it incorporates the effective population fraction as a parameter. Within the Markov Chain Monte Carlo (MCMC) algorithm, Unscented Kalman Filter (UKF) may be used to evaluate the likelihood approximately. UKF is a suitable strategy in many cases, but it is not well-suited to deal with non-negativity restrictions on the state variables. To overcome this difficulty, we modify the UKF, conveniently truncating Gaussian distributions, which allows us to deal with such restrictions. We use official infection notification records to analyze the first 22 weeks of infection spread in each of the 27 countries of the European Union (EU). It is known that such records are the primary source of information to assess the early evolution of the pandemic and, at the same time, usually suffer underreporting and backlogs. Our model explicitly accounts for uncertainty in the dynamic model parameters, the dynamic model adequacy, and the infection observation process. We argue that this modeling paradigm allows us to disentangle the role of the contact rate, the effective population fraction, and the infection observation probability across time and space with an imperfect first principles model. Our findings agree with phylogenetic evidence showing little variability in the contact rate, or virus infectiousness, across EU countries during the early phase of the pandemic, highlighting the advantage of incorporating the effective population fraction into pandemic modeling for heterogeneity in both human behavior and reporting. Finally, to evaluate the consistency of our data assimilation method, we performed a forecast that adequately fits the actual data. Data-driven and model-based epidemiological studies aimed at learning the number of people infected early during a pandemic should explicitly consider the behavior-induced effective population effect. Indeed, the non-isolated, or effective, fraction of the population during the early phase of the pandemic is time-varying, and first-principles modeling with quantified uncertainty is imperative for an adequate analysis across time and space. We argue that, although good inference results may be obtained using the classical SEIR type model, the model posed in this work has allowed us to disentangle the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union from official infection notification records. [ABSTRACT FROM AUTHOR]

In reliability engineering or medical studies, failure of units or individuals may occurring due to several failure modes which is known by competing risks model. In this paper, we have adopted a competing risks model under consideration that, the failure time has generalized inverted exponential distribution (GIED) and the failure modes are independent and partially observed. Under type-II progressive censoring scheme (type-II PCS), the experiment is designed as a quite popular scheme for collecting data specially, for a highly reliable product. The point estimates of the model parameters are formulated under classical approach (maximum likelihood and bootstrap) and Bayesian approach with respect to the symmetric squared error loss function. Also, the approximate confidence intervals under a large sample theory, bootstrap technique's and Markov chain Monte Carlo method (MCMC) are constructed. The estimators are assessed and compared with building the simulation study. Analysis a set of type-II PCS competing risks mortality data for illustration purpose. [ABSTRACT FROM AUTHOR]

The study aims to showcase machine learning techniques in the application of medical datasets for improving identification of correlations and relationships between variables, which will lead to more informed decision-making. Unlike other studies, intensive statistical modelling is used to understand and find the effective of variables cause to lead death due to Covid-19. Due to large dataset, not common approaches derive us to ideal conclusion. Furthermore, Bayesian technique is applied to generate predictive posterior distributions of the unknown parameters in the model in neural network as well as logistic regression, which helps us to avoid overfitting in machine learning applications and have additional measurements in assessing fitted model performance. According to the results extracted from the statistical analysis, the Bayesian neural network demonstrated superior performance in terms of classification measurements such as AUC (84.66%), F1 (87.11%), Precision (88.29%), and Recall (85.96%). The Bayesian logistic regression also performed well, but with slightly lower scores, achieving AUC (83.07%), F1 (85.59%), Precision (84.55%), and Recall (85.59%). In contrast, logistic regression (MLE) technique had the worst performance with very low scores (AUC = 52.38%, F1 = 57.55%, Precision = 57.01%, Recall = 58.10%). Regarding the variables' association with mortality, stepwise forward selection helped to identify seven significant variables. Age was found to be the most significant variable in predicting the probability of dying, with patients in the age group of (18–44) having 12 times higher odds, patients in the age group of (45–64) having 123 more odds, and patients above 65 years old having 436 times more chance to die compared to patients below 18 years old. Severe coughing was also significant with 7.26 odds, and patients suffering from diabetes had 2.82 times more chance to die. Moreover, SpO2 contributed to a decrease of 20% in the relative risk of dying from Covid-19 disease. Gender and Smoking did not show a significant association with mortality. Finally, the Bayesian approach showed higher sensitivity and specificity than the classic neural network. [ABSTRACT FROM AUTHOR]

• The proposed method (P-SuS) generalises subset simulation from, to the capability of handling distributional responses. • P-SuS can be used in reliability analyses with partially-converged simulations, probabilistic and surrogate models. • The algorithm can be used in lieu of subset simulation in reliability analysis, optimisation, and pre-calibration. Reliability analysis can be performed efficiently through subset simulation. Through Markov chain Monte Carlo, subset simulation progressively samples from the input domain of a performance function (typically a computer model) to find the failure domain, that is, the set of input configurations that result in an output higher than a prescribed threshold. Recently, a probabilistic framework for numerical analysis was proposed, whereby computation is treated as a statistical inference problem. The framework, called probabilistic numerics, treats the output of a computer code as a random variable. This paper presents a generalisation of subset simulation, which enables reliability analysis for probabilistic numerical models. The advantages and challenges of the method are discussed, and an example with industrial application is presented. [ABSTRACT FROM AUTHOR]

Concerns that impair human societies frequently include a heavy dependence on petroleum and coal and emissions of greenhouse gases. Thus, adopting renewable energy sources, such as wind power, has become a practical solution to this problem. Therefore, to carry out the research on wind velocity energy, a time-series structure is necessary. This study uses the Markov chain Monte Carlo approach and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to estimate short-term and long-term sustained winds. The significance of building a wind energy system is initially discussed, after which a wind velocity time-series framework based on a SARIMA is presented, followed by a short-term and Long-term wind speed projection. Furthermore, a methodology utilizing the Markov chain Monte Carlo method (MCMC) is suggested to establish a wind energy time-series analysis. This framework draws a Markov chain for time-series data on wind energy to maintain stochasticity and realize the probability transition matrix. Gibbs sampling is employed as well. The model's forecasting abilities were tested using the original database and various efficiency assessment measures, including Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) with efficiency of 13.09 and 1.03. In this study, a framework with the highest KGE and WI as well as the lowest RMSE and MAE was chosen. The findings demonstrate that the approach used in the operation provides outstanding predictability. [ABSTRACT FROM AUTHOR]

Frequentist semiparametric theory has been used extensively to develop doubly robust (DR) causal estimation. DR estimation combines outcome regression (OR) and propensity score (PS) models in such a way that correct specification of just one of two models is enough to obtain consistent parameter estimation. An equivalent Bayesian solution, however, is not straightforward as there is no obvious distributional framework to the joint OR and PS model, and the DR approach involves a semiparametric estimating equation framework without a fully specified likelihood. In this paper, we develop a fully semiparametric Bayesian framework for DR causal inference by bridging a nonparametric Bayesian procedure with empirical likelihood via semiparametric linear regression. Instead of specifying a fully probabilistic model, this procedure is only realized through relevant moment conditions. Crucially, this allows the posterior distribution of the causal parameter to be simulated via Markov chain Monte Carlo methods. We show that the posterior distribution of the causal estimator satisfies consistency and the Bernstein–von Mises theorem, when either the OR or PS is correctly specified. Simulation studies suggest that our proposed method is doubly robust and can achieve the desired coverage rate. We also apply this novel Bayesian method to a real data example to assess the impact of speed cameras on car collisions in England. [ABSTRACT FROM AUTHOR]

Tris(1,3-dichloro-2-propyl) phosphate (TDCIPP), Tris (1-chloro-2-propyl) phosphate (TCIPP) and tris (2-chloroethyl) phosphate (TCEP) are three widely used organophosphate flame retardants (OPFRs) being frequently detected in human body fluids. Although OPFRs are being detected in human beings, the toxicological effects of their exposure are not clearly understood due to limited data. For this, a physiologically based kinetic model (PBK) was developed in MCSIM integrated with R studio and validated in rats to understand the toxicokinetics of OPFRs for the first time. The model required the enterohepatic recirculation (EHR) mechanism which was included to explain the non-linear data. Model parameters were optimized using the Bayesian framework (Markov Chain Monte Carlo) along with a visual fitting to explain toxicokinetic data. Goodness-of-fit was calculated to evaluate model predictability power in Rstudio. The model can appropriately predict the concentration of OPFRs in several organs like plasma, urine, kidney, etc. within 1–2-fold of experimental data. Slow elimination of OPFRs was observed from adipose tissue and brain at late time points, showing their potential to accumulate upon daily exposure. The use of PBK was demonstrated by reconstructing the oral exposure equivalent to the in-vitro toxic dose to support neurotoxic risk assessment. This version of PBK can be extrapolated to human for toxicological risk assessment. Nonetheless, further investigation is required to understand whether these chemicals follow similar kinetics in humans, which could lead to a greater risk to human health. The model will be available to access through Rshiny using GIThub soon, InSilicoVida/Flame-Retardant-PBPK-Model: It contains organophosphate flame retardant (OPFRs) PBK for TDCIPP, TCIPP and TCEP (github.com). • Multi-compartment PBPK model developed for data-deficient organophosphate flame retardants for the first time. • Current PBPK model with limited in-vivo data along with in-vitro successfully explained the kinetics. • Enterohepatic recirculation (EHR) was included in model to show slow elimination. • IVIVE-based PBPK utilized to reconstruct the equivalent oral in-vivo exposure for neurotoxic risk assessment in rats. [ABSTRACT FROM AUTHOR]

Ensembles of networks arise in various fields where multiple independent networks are observed, for example, a collection of student networks from different classes. However, there are few models that describe both the variations and characteristics of networks in an ensemble at the same time. In this manuscript, we propose to model ensembles of networks using a Dirichlet Process Mixture of Exponential Random Graph Models (DPM-ERGMs), which divides an ensemble into different clusters and models each cluster of networks using a separate Exponential Random Graph Model (ERGM). By employing a Dirichlet process mixture, the number of clusters can be determined automatically and changed adaptively with the data provided. Moreover, in order to perform full Bayesian inference for DPM-ERGMs, we develop a Metropolis-within-slice sampling algorithm to address the problem of sampling from the intractable ERGMs on an infinite sample space. We also demonstrate the performance of DPM-ERGMs with both simulated and real datasets. • Clustering multiple network samples using an infinite mixture of ERGMs without requiring a pre-specified number of clusters in advance. • Describing the diverse within-network dependencies using ERGMs. • Providing full Bayesian inference based on true likelihood, adjusted pseudo likelihood and pseudo likelihood. [ABSTRACT FROM AUTHOR]

• A Deep Operator Network (DeepONet) for data-driven transient response prediction. • The Bayesian DeepONet enables a reliable prediction via uncertainty quantification. • The Bayesian DeepONet enables learning even when data is scarce. • The Probabilistic DeepONet enables a reliable prediction at virtually no extra cost This paper proposes a novel data-driven method for the reliable prediction of the power grid's post-fault trajectories, i.e., the power grid's dynamic response after a disturbance or fault. The proposed method is based on the recently proposed concept of Deep Operator Networks (DeepONets). Unlike traditional neural networks that learn to approximate functions, DeepONets are designed to approximate nonlinear operators, i.e., mappings between infinite-dimensional spaces. Under this operator framework, we design a novel and efficient DeepONet that (i) takes as inputs the trajectories collected before and during the fault and (ii) outputs the predicted post-fault trajectories. In addition, we endow our method with the much-needed ability to balance efficiency with reliable/trustworthy predictions via uncertainty quantification. To this end, we propose and compare two novel methods that enable quantifying the predictive uncertainty. First, we propose a Bayesian DeepONet (B-DeepONet) that uses stochastic gradient Hamiltonian Monte-Carlo to sample from the posterior distribution of the DeepONet trainable parameters. Then, we design a Probabilistic DeepONet (Prob-DeepONet) that uses a probabilistic training strategy to enable quantifying uncertainty at virtually no extra computational cost. Finally, we validate the proposed methods' predictive power and uncertainty quantification capability using the New York-New England power grid model. [ABSTRACT FROM AUTHOR]

For most Bayesian inference problems that are of interest, solving for the model parameter posterior probability distribution remains to be the main challenge. A typical technique entails using Markov chain Monte Carlo (MCMC), in order to obtain an approximation of the posterior distribution. However, one drawback of MCMC is that it usually requires solving the forward problem multiple times, which could be infeasible, if the forward model is computationally expensive. In this paper, a computationally cheap surrogate model is developed using the adaptive neuro-fuzzy inference system with a fuzzy c-means initialization (ANFIS-FCM). The optimal number of fuzzy rules is chosen via a proposed algorithm based on the pairing frequency clustering validity index. The effectiveness of the ANFIS-FCM model is tested on inferring reaction rate parameters in a 3-node network toy problem, which mimics idealized combustion reaction networks. Using multiple error measures, it is shown that the developed ANFIS-FCM model is capable of constructing accurate surrogate models at a minimal computational cost. [ABSTRACT FROM AUTHOR]

Thermal fatigue cracking is a common problem for boiler waterwall tubes in power plants, resulting the risk of unscheduled plant shutdown. Developing an effective degradation model to predict the future condition of waterwall tubes is vital to assess boiler remaining life. Even though many numerical studies are available in the literature for understanding the physical mechanism (thermal fatigue cracking), the development of predictive models for future risks that can inform decision making are limited. One of the key impediments for developing predictive models is the lack of data. In the case of boiler critical subsystems, very few failures (if any) are recorded in its history. In addition, the number of inspections is limited due to access difficulties. Thus, in many practical cases, the condition data available for degradation modelling is sparse with both infrequent and (spatially) incomplete inspections. This paper presents the development of a stochastic degradation model prediction of thermal fatigue cracking severity in a boiler waterwall when the data is limited. The time evolution of the condition indicator for each waterwall tube is modelled using Markov Models and Bayesian identification algorithms are used to tractably address the large amount of missing data. The methodology presented in this paper employs a Markov Chain Monte Carlo (MCMC) scheme to account for the parameter uncertainty in the presence of missing data. Moreover, this paper also incorporates a novel grouping strategy in which the neighbouring components are grouped to tackle the problem of (spatial) data sparsity. The degradation modelling and prediction tools developed in this paper will support renewal decision making by using available onsite asset data and knowledge. A real-world case study of a boiler waterwall operating in an Australian power industry is also presented. It is found that the waterwall degradation appears to be slowing, but an additional inspection is recommended to confirm the trend. [ABSTRACT FROM AUTHOR]

• Corrected an error (posterior risk of Bayesian estimation under the entropy loss function) in Ali et al. [10]. • Derived the corrected posterior risk of Bayesian estimation under the entropy loss function. • Derived posterior risk of Bayesian estimation for binomial distribution under the entropy and squared error loss function. • Monte Carlo simulation example and application example are provided for illustrative purposes. Scholars have discussed the Bayesian estimation and their corresponding posterior risks for the Lindley distribution parameter, respectively, using seven different loss functions, where the posterior risk under entropy loss function (ELF) is not correct. In this paper, we will derive the Bayesian estimation and its corrected posterior risk under the entropy loss function. Moreover, the Bayesian estimations and their posterior risks of binomial distribution parameter under the entropy loss function and squared error loss function are respectively developed. Monte Carlo simulation example and application example are provided for illustrative purposes, and results are compared based on posterior risk. [ABSTRACT FROM AUTHOR]

We report the results of source apportionment of combustion-derived black carbon (BC), in PM 2.5 aerosols collected from the East Asian continental outflow region at two islands; Okinawa and Fukue during the period from October 2009 to May 2010. The 14C contents of BC from Okinawa (Cape Hedo) and Fukue islands (Fukue) varied from 24.4 to 34.2 pMC and from 18.1 to 43.0 pMC, respectively. Source apportionment of BC was conducted by using a simple 14C mass balance model and a dual-isotope Bayesian Markov chain Monte Carlo model. Biomass combustion contributions (e.g., wood fuel and crop residues) were 23–31 % (mean 27 ± 3 %) at Cape Hedo and 15–38 % (mean 27 ± 12 %) at Fukue. Both sites were equally affected by biomass combustion, whose levels are similar to those observed in the hotspot regions (megacities) in China. Fossil fuel contributions (e.g., oil, gasoline, and diesel) were 69–77 % (mean 74 ± 3 %) with 2–69 % from coal and 3–76 % from liquid fossil at Cape Hedo, and 55–85 % (mean 73 ± 12 %) at Fukue with 3 % from coal and 53–56 % from liquid fuel for the early winter. Those variations for both sites showed significantly different source region-specific trends and this trend was also consistent with the results of the air mass trajectory analysis with a higher contribution of coal-BC from North China including Beijing, and East China including Shanghai. In spring, we found a higher contribution of liquid fossil fuel combustion (e.g., traffic) over the East China Sea, including Pacific air masses, suggesting that the possible contribution of ship emissions was relatively high. Although our results for isotopes-based source apportionment of BC are preliminary, these findings suggest that long-term monitoring of BC aerosols, even in remote sites of Asian outflows, and source-specific descriptions of BC are important to improve climate models. We also recommend further measurements for implementing regionally tailored mitigation. [ABSTRACT FROM AUTHOR]

Since the robotic capturing of a space tumbling object requires a rapidly accurate prediction of the object motion, this paper addresses the nonlinear multi-step motion prediction issue of the object based on machine learning theory and proposes a novel prediction approach, in which the prediction efficiency and the prediction accuracy are main focuses. To improve the prediction efficiency, sparse Gaussian processes are employed for the multi-step motion prediction, only providing the historical data of the object motion states. The sparse pseudo training data continuously optimized from real historical data makes it feasible for real-time predictions. To improve the prediction accuracy, a heuristic optimizer, Markov chain Monte Carlo, is integrated within sparse Gaussian processes to overcome the sensitivity problem caused by random initializations during the training process, thus avoids the local optimal training results. Then, a high-quality sparse pseudo training data is obtained to construct the predictive distributions during the testing process. The benchmark example and comparisons of several motion prediction applications illustrate the correctness and effectiveness of the proposed approach, and the experimental validation demonstrates its real-world potential. [ABSTRACT FROM AUTHOR]

An accurate model of proton exchange membrane fuel cell (PEMFC) is essential for its characterization, performance analysis, and design of optimal control strategies. However, due to various disturbances and measurement noise in practical operation, a PEMFC presents high stochasticity and parameter uncertainty. Therefore, a semi-empirical output voltage model parameter estimation method based on variational Bayes (VB) PEMFC is proposed and combined with the Sobol sensitivity analysis method to analyze the relationship between the parameters of the model to be identified and the effect of the output voltage of the model under different noise and operating conditions. The numerical results show that the VB method is able to quantify the uncertainty of the parameter estimation results and has higher computational accuracy compared with the expectation maximization (EM) method. Compared with the Markov chain Monte Carlo (MCMC) method, the VB method is able to greatly reduce the computational effort and takes less time while satisfying the accuracy. Meanwhile, the sensitivity of the model parameters to be identified to the output voltage of the model under different noise and operating conditions is quantified using the Sobol method, which explains the variation of the posterior probability distribution results obtained using the VB method under different noise and operating conditions. • Variational Bayes method is proposed to estimate parameters of PEMFC model. • Posterior distribution is obtained by automatic differential variational inference. • The uncertainty caused by noise and interference is quantified. • Higher calculation accuracy compared with EM method. • Higher computational efficiency compared with MCMC method. [ABSTRACT FROM AUTHOR]

This paper considers the identification of the Hammerstein system with immeasurable process noise. The complexity of the Hammerstein system makes it difficult to obtain accurate mathematical expressions of the parameters, or even impossible to obtain accurate mathematical expressions at all. In this contribution, we cast the Hammerstein system parameter identification problem as a posterior parameter estimation problem and take a sampling and Stein variational inference viewpoint to solve it. Improved Stein variational gradient descent(ISVGD)algorithm is proposed in posterior parameter calculation. Compared with other methods, not only the prior distribution of parameters but also the proposal distribution of parameters is considered. In other words, The posterior information is enriched and the parameter identification accuracy is improved. At the same time, ISVGD and reversible jump markov chain monte carlo (RJMCMC) algorithms are used (called ISVGD-RJMCMC algorithm) in the structure detection problem. In the algorithm, the correct basis function number k can be found in the parameter estimation. It tends to be accurate but is slow to converge. Three simulation examples were given to demonstrate the proposed algorithm's effectiveness. Furthermore, the performances of these approaches were analyzed, including parameter estimation accuracy and error, system order estimation and parameter convergence analysis. • Stein variational inference is used in Hammerstein system identification. • Improved Stein variational gradient descent(ISVGD)algorithm is proposed. • ISVGD-RJMCMC algorithm is used in the structure detection problem. [ABSTRACT FROM AUTHOR]

• A multielement polynomial chaos expansion based Kriging model is proposed to bypass non-smooth engineering models. • The proposed metamodel is shown proficient for online Bayesian inference. • Model parameter inference is conducted by adaptive Markov chain Monte Carlo with delayed rejection. • The proposed model is validated with a highly nonlinear benchmark function. • The developed model is used for inference of thermal desorption spectroscopy tests. This paper presents a surrogate modelling technique based on domain partitioning for Bayesian parameter inference of highly nonlinear engineering models. In order to alleviate the computational burden typically involved in Bayesian inference applications, a multielement Polynomial Chaos Expansion based Kriging metamodel is proposed. The developed surrogate model combines in a piecewise function an array of local Polynomial Chaos based Kriging metamodels constructed on a finite set of non-overlapping subdomains of the stochastic input space. Therewith, the presence of non-smoothness in the response of the forward model (e.g. nonlinearities and sparseness) can be reproduced by the proposed metamodel with minimum computational costs owing to its local adaptation capabilities. The model parameter inference is conducted through a Markov chain Monte Carlo approach comprising adaptive exploration and delayed rejection. The efficiency and accuracy of the proposed approach are validated through two case studies, including an analytical benchmark and a numerical case study. The latter relates the partial differential equation governing the hydrogen diffusion phenomenon of metallic materials in Thermal Desorption Spectroscopy tests. [ABSTRACT FROM AUTHOR]

A block Markov chain is a Markov chain whose state space can be partitioned into a finite number of clusters such that the transition probabilities only depend on the clusters. Block Markov chains thus serve as a model for Markov chains with communities. This paper establishes limiting laws for the singular value distributions of the empirical transition matrix and empirical frequency matrix associated to a sample path of the block Markov chain whenever the length of the sample path is Θ (n 2) with n the size of the state space. The proof approach is split into two parts. First, we introduce a class of symmetric random matrices with dependent entries called approximately uncorrelated random matrices with variance profile. We establish their limiting eigenvalue distributions by means of the moment method. Second, we develop a coupling argument to show that this general-purpose result applies to the singular value distributions associated with the block Markov chain. [ABSTRACT FROM AUTHOR]

In eastern Crete, several normal faults trend both NNE-SSW and E-W. Most of them form impressive bedrock fault scarps indicating ongoing extension in the Hellenic fore-arc. No earthquakes from historical and instrumental catalogues can be directly associated with these faults. Geomorphologic profiles across five different faults in sites well-preserved from erosion, deposition or human intervention revealed displacements of ∼6.9 m–∼30 m. Following a robust site selection at the Lastros and Orno faults, we mapped different categories of weathered horizontal ribbons along the bedrock fault plane based on colour contrast, lichen colonisation and karstic features that affect fault plane roughness, features that altogether are interpreted as earthquake horizons. The data indicate coseismic displacements of 15–35 cm for the last three earthquakes at the Lastros fault and 10–20 cm for the last two earthquakes at the Orno fault. To constrain the age of these earthquakes and to estimate millennial fault slip rates, we determined cosmogenic 36Cl concentrations from vertical profiles composed of 24 samples from the exposed Lastros fault plane and 7 samples of the Orno fault plane. Bayesian Markov Chain Monte Carlo (MCMC) modelling implies that the last three earthquakes at the Lastros fault, occurred within the last 1.8 ± 1.2 kyr (EQ1 = 1.1 ± 1.1 kyr, EQ2 = 1.4 ± 1.1 kyr, EQ3 = 1.8 ± 1.2 kyr). Furthermore, Lastros' slip rate varied with a rate of 0.6 ± 0.3 mm/yr during the last 1.8 ± 1.2 kyr, preceded by a rate of 1.1 ± 0.4 mm/yr from ∼1.8 to 8 kyr; suggesting periods of earthquake clustering. At the Orno fault, the last two earthquakes occurred within the last ∼3.5 kyr accompanied with a slip rate of 0.16+0.06 -0.02 mm/yr during postglacial times. This confirms for the first time based on absolute dating, that both NNE-SSW and E-W trending neighbouring faults in Crete were active during the Holocene. Most of the 13.2 m high Orno fault scarp was exhumed during glacial times, with an apparent slip rate of 0.28+0.12 -0.07 mm/yr. The upper part of the Lastros fault scarp (9–12 m) and the apparent age of the four Lastros footwall samples (∼17.9 kyr, ∼26.0 kyr, ∼29.9 kyr, ∼37.4 kyr) underline the long preservation throughout glacial times as well. These findings show that the general hypothesis of predominantly postglacial bedrock fault scarps needs to be regarded with caution at lower latitudes and/or low altitudes (<1000 m), since degradation processes are weaker. • Cosmogenic dating on postglacial scarps confirms that both NNW-SSE and E-W trending faults were active during the Holocene. • M∼6.4 palaeoearthquakes have a mean recurrence interval of 200–400 yrs on the Lastros and 600–2000 yrs on theOrno fault. • Postglacial slip rate variability of the Lastros fault between ∼0.6 and ∼1.1 mm/yr. • Uppermost free-faces preserved during glacial times, implying weak degradation in lower latitudes and <1000 m altitude. [ABSTRACT FROM AUTHOR]

• This research focuses on giving a comparative examination of the variables contributing to various types of crashes between automated vehicles and conventional vehicles. • A Bayesian Network (BN) fitted using the Markov Chain Monte Carlo (MCMC) was used to achieve the study objective. • The study used four years (2017–2020) of AV and conventional vehicle crash data on California roads. The AV crash dataset was acquired from the California Department of Motor Vehicle, while conventional vehicle crashes were obtained from the Transportation Injury Mapping System database. • Along with other findings, our comparative analysis of the associated features suggests that AVs are 43% more likely to be involved in rear-end crashes. • AVs are 16% and 27% less likely to be involved in sideswipe/broadside and other types of collisions (head-on, hitting an object, etc.), respectively, when compared to conventional vehicles. • Despite the fact that AVs increase road safety in most kinds of crashes by reducing human error leading to vehicle crashes, the current state of the technology demonstrates that safety elements still need development. Introduction: Automated vehicle (AV) technology is a promising technology for improving the efficiency of traffic operations and reducing emissions. This technology has the potential to eliminate human error and significantly improve highway safety. However, little is known about AV safety issues due to limited crash data and relatively fewer AVs on the roadways. This study provides a comparative analysis between AVs and conventional vehicles on the factors leading to different types of collisions. Method: A Bayesian Network (BN) fitted using the Markov Chain Monte Carlo (MCMC) was used to achieve the study objective. Four years (2017–2020) of AV and conventional vehicle crash data on California roads were used. The AV crash dataset was acquired from the California Department of Motor Vehicles, while conventional vehicle crashes were obtained from the Transportation Injury Mapping System database. A buffer of 50 feet was used to associate each AV crash and conventional vehicle crash; a total of 127 AV crashes and 865 conventional vehicle crashes were used for analysis. Results: Our comparative analysis of the associated features suggests that AVs are 43% more likely to be involved in rear-end crashes. Further, AVs are 16% and 27% less likely to be involved in sideswipe/broadside and other types of collisions (head-on, hitting an object, etc.), respectively, when compared to conventional vehicles. The variables associated with the increased likelihood of rear-end collisions for AVs include signalized intersections and lanes with less than 45 mph speed limit. Conclusions: Although AVs are found to improve safety on the road in most types of collisions by limiting human error leading to vehicle crashes, the current state of the technology shows that safety aspects still need improvement. [ABSTRACT FROM AUTHOR]

The problem of testing the product units under stress higher than normal stress conditions is widely used in reliability analysis specially, in a high reliable products to saving time and cost which is known by accelerated life tests (ALTs) model. In this paper, we are adopted partially constant-stress ALTs model for product units have generalized inverted exponential (GIE) lifetime. And, the test is running under progressive Type-II censoring scheme. The model parameters are estimated with maximum likelihood and Bayes methods. The corresponding asymptotic confidence intervals as well as credible intervals are also constructed. The theoretical results are assessed and compared through Monte Carlo simulation study. The numerical data set is analyzed under the proposed model for illustrative purpose. Finally, we reported some comments about numerical computation. [ABSTRACT FROM AUTHOR]

We introduce a novel procedure to perform Bayesian non-parametric inference with right-censored data, the beta-Stacy bootstrap. This approximates the posterior law of summaries of the survival distribution (e.g. the mean survival time). More precisely, our procedure approximates the joint posterior law of functionals of the beta-Stacy process, a non-parametric process prior that generalizes the Dirichlet process and that is widely used in survival analysis. The beta-Stacy bootstrap generalizes and unifies other common Bayesian bootstraps for complete or censored data based on non-parametric priors. It is defined by an exact sampling algorithm that does not require tuning of Markov Chain Monte Carlo steps. We illustrate the beta-Stacy bootstrap by analyzing survival data from a real clinical trial. • The beta-Stacy bootstrap performs Bayesian non-parametric inference with censored data. • It generalizes and unifies other common Bayesian bootstrap algorithms. • Code to implement the beta-Stacy bootstrap is available at https://github.com/andreaarfe/. [ABSTRACT FROM AUTHOR]

Data integrity and security protection are needed in the Internet of Things. IOTA technology with a Directed Acyclic Graph (DAG) structure is a solution to realize secure and scalable data transactions. Recent research IOTA is still faced with the issue of being vulnerable to splitting attacks and left-behind transactions. The splitting attack causes the network to confirm conflict transactions. Then, left-behind transactions cause the network to generate transactions that will not be confirmed. The selection tip weighted random walk (WRW) algorithm uses the Markov Chain Monte Carlo (MCMC) to overcome these two issues by applying the appropriate bias parameter (α). However, when the α is too large, it will produce a lot of left-behind transactions. Determining the optimal value of α is still an important research topic today. An E-IOTA study that gives several α values statically with random selection but can still produce more left-behind transactions than pure WRW. This paper proposes an optimization of the tip selection algorithm (DA-IOTA) to determine the optimal alpha (α) using an approach to dynamically determine each WRW step. The experimental results show that DA-IOTA produces fewer left-behind transactions than MCMC (WRW) and E-IOTA which use α parameters statically. [ABSTRACT FROM AUTHOR]

Handling multimodality that commonly arises from complicated statistical models remains a challenge. Current Markov chain Monte Carlo (MCMC) methodology tackling this subject is based on an ensemble of chains targeting a product of power-tempered distributions. Despite the theoretical validity of such methods, practical implementations typically suffer from bad mixing and slow convergence due to the high-computation cost involved. In this work we study novel extensions of the t-walk algorithm, an existing MCMC method that is inexpensive and invariant to affine transformations of the state space, for dealing with multimodal distributions. We acknowledge that the effectiveness of the new method will be problem dependent and might struggle in complex scenarios; for such cases we propose a post-processing technique based on pseudo-marginal theory for combining isolated samples. • Multimodal models are not rare in many Bayesian inference problems. • Current methods, such as MCMC, can handle multimodality but at high cost. • Using penalised moves within existing algorithms may help exploring multiple modes. • An alternative is to combine pseudo-samples from disjoint regions. [ABSTRACT FROM AUTHOR]

A vast amount of methods have been developed to make inferences for volatility data, taking into account the stylized facts of rate/return data. However, the common problem is that the latent parameters of many volatility models are high-dimensional and analytically intractable. Therefore, their inference procedure requires numerical approximations using intensive computational techniques, as the Markov chain Monte Carlo, Laplace, and particle filter methods. A common strategy to overcome this problem is model linearization. This approach consists of writing the stochastic volatility model as a linear Gaussian state-space model, leading to an approximated marginal likelihood using the Kalman filter, reducing the problem's dimensionality. This paper proposes a new filtering inference procedure with an integrated likelihood for a Generalized Error Distribution (GED) state-space volatility model without model linearization. Also, we evaluate the quality of our method approximation and introduce an approximated smoothing procedure. We use the Bayesian methods for making the inference of static parameters and perform a simulation exercise to study the estimators' properties. Our results show that the proposed model can be reasonably estimated, and the approximation of our method is reasonable. Furthermore, we provide a case study of the pound/dollar and real/dollar exchange rate to illustrate our approach's performance. For the real/dollar time series, the model captures a high volatility pattern due to the COVID-19 pandemic. • A new filtering procedure (the CUP) with an approximated marginal likelihood for a stochastic volatility model. • The inference is fast and easy to implement under the Bayesian approach without model linearization. • The high-dimensional hidden states are integrated out and sampled in blocks with an approximated smoothing procedure. • For the log-returns is used a GED model, but it can be easily extended to other skew and heavy-tailed distributions. • The CUP can also be applied to other models. [ABSTRACT FROM AUTHOR]

Typically, the parametric proportional hazard (PH) model is used to examine data on mortality occurrences. Competing risks are prevalent in health information, making it difficult to manage time to event data in clinical investigations. A Bayesian framework is being developed for managing conflicting risk occurrences in clinical data. The objective of this study is to identify the variables that affect patients' odds of surviving peripheral blood stem-cell transplantation, a therapy option for life-threatening blood disorders. In addition, we want to implement a Bayesian model capable of analysing time-to-event data in the context of competing risk. In this research, we analyse failure reasons in the setting of competing risk models using the generalised log-logistic with right-censored scheme. We present competing risks models for censored survival data in the presence of explanatory variables, where each system contains more than one component in series. We assume that each component's survival time follows a generalized log-logistic distribution. We obtain Bayesian estimates of the component's lifetime distribution parameters and regression coefficients. We present a comprehensive Markov chain Monte Carlo (McMC) method to evaluate the estimators' convergence diagnostics. A real-survival data set dealing with stem-cell transplants demonstrated the model's flexibility and advantages. [ABSTRACT FROM AUTHOR]

We present a new continuous lifetime model with four parameters by combining the Lomax and the Weibull distributions. The extended odd Weibull Lomax (EOWL) distribution is what we'll call it. This new distribution possesses several desirable properties thanks to the simple linear representation of its hazard rate function, moments, and moment -generating function, with stress-strength reliability that are provided in a simple closed forms. The parameters of the EOWL model are estimated using classical methods such as the maximum likelihood (MLE) and the maximum product of spacing (MPS) and estimated also but using a non-classical method such as Bayesian analytical approaches. Bayesian estimation is performed using the Monte Carlo Markov Chain method. Monte Carlo simulation are used to assess the effectiveness of the estimation methods throughout the Metropolis Hasting (MH) algorithm. To illustrate the suggested distribution's effectiveness and suitability for simulating real-world pandemics, we used three existing COVID-19 data sets from the United Kingdom, the United States of America, and Italy which are studied to serve as illustrative examples. We graphed the P-P plots and TTT plots for the proposed distribution proving its superiority in a graphical manner for modelling the three data sets in the paper. [ABSTRACT FROM AUTHOR]

In cognitive radio networks (CRNs), secondary users (SUs) are permitted to use the available spectrum in opportunity ways to optimize spectrum utilization. As legal spectrum owners, primary users (PUs) can always interrupt SUs' transmissions when they have transmission needs. To increase SUs' transmission continuity while guaranteeing PUs' transmission quality, we propose a preemption backoff mechanism (PBM) with backoff threshold m and backoff parameter f. Under the centralized spectrum sharing structure, we construct a discrete-time queueing model by developing a 3-dimensional Markov-chain (3DMC). Through the model analysis, the system's steady-state probability distribution is obtained. To assess the comprehensive performance of our proposed mechanism, we deduce various important expressions of performance indices and get the corresponding performance figures. At last, we design a cost function by compromising various performance indices and find the optimum parameter combination under the lowest system cost through the Transient Search Optimization (TSO) algorithm. The numerical and simulation results illustrate our proposed PBM can improve SUs' transmission continuity. For example, when m = 1 and f = 0.05 , under the parameter settings given by the experiments, the proposed PBM can reduce SU packets' outage rate and improve throughput by about 60% at most. [ABSTRACT FROM AUTHOR]

The accuracy of models to capture the uncertainty of renewables significantly affects the planning and operation of renewable energy-based stand-alone (REB-SA) microgrids. This paper aims to first study different stochastic and deterministic models for renewables, then evaluate the performance of an REB-SA microgrid planning problem and provide qualitative and quantitative comparisons. A modified Metropolis-coupled Markov chain Monte Carlo simulation is considered for the first time in the planning of an REB-SA microgrid to predict the behavior of renewables with minimum iterations. The modified model is benchmarked against two prevalent models including the retrospective model with worst-case scenarios and the Monte Carlo simulation. The operations of three designed microgrids (by these three methods) are evaluated using the last three-year historical data of a city in northern Sweden including solar radiation, wind speed, the water flow of a river, and load consumption. The impacts of the considered methods on using PV panels and hydrogen systems are investigated. The results verify that the modified model decreases the risk of planning and operation of an REB-SA microgrid from the energy and power shortage viewpoints. Moreover, the designed microgrid with the modified model can cope with all possible scenarios from economic, technical, and environmental viewpoints. • Uncertainties of renewable energy are major concerns for replacing fossil-fuel energy. • A modified Metropolis-coupled MCMCS is applied to the microgrid planning problem. • Uncertainty modeling methods on microgrid planning and operation are benchmarked. • Microgrid planning is highly affected by different uncertainty modeling methods. • The planning results besides the benchmarks show economic and reliability improvement. [ABSTRACT FROM AUTHOR]

• We introduce different possible combinations of Markov Chain Monte Carlo algorithms and importance sampling schemes. • The different variants address computational challenges arising in real-world applications. • We introduce different strategies for designing cheaper schemes, for instance, recycling samples. • Numerical experiments show the benefits of the proposed schemes as compared to other benchmark methods. • Related Matlab and Python code is also available. Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of layered adaptive importance sampling algorithms, which is a family of adaptive importance samplers where Markov chain Monte Carlo algorithms are employed to drive an underlying multiple importance sampling scheme. The modular nature of the layered adaptive importance sampling scheme allows for different possible implementations, yielding a variety of different performances and computational costs. In this work, we propose different enhancements of the classical layered adaptive importance sampling setting in order to increase the efficiency and reduce the computational cost, of both upper and lower layers. The different variants address computational challenges arising in real-world applications, for instance with highly concentrated posterior distributions. Furthermore, we introduce different strategies for designing cheaper schemes, for instance, recycling samples generated in the upper layer and using them in the final estimators in the lower layer. Different numerical experiments show the benefits of the proposed schemes, comparing with benchmark methods presented in the literature, and in several challenging scenarios. [ABSTRACT FROM AUTHOR]

While it remains uncertain whether excursions in the stable carbon isotopic composition of Ediacaran marine carbonate (δ13C carb) represent globally synchronous events (or a direct measure of ocean carbon cycling), the absence of widely distributed and readily preservable fauna, and the presence of several iconic carbon isotope excursions (CIEs), has sustained δ13C carb correlation as the primary means to establish relative time relationships for Ediacaran successions. Here we present an Ediacaran global δ13C carb composite built with a dynamic time warping (DTW) time-normalization algorithm that generates libraries of least-squares alignments between chemostratigraphic records of unequal length and distinct sediment accumulation rates. When developing a δ13C carb composite for each of 16 globally distributed Ediacaran paleo-depositional regions, we selected high Pearson r alignments that conformed with published geological guidance about the correlation of constituent sections. When applying DTW to align these regional algorithmic composites into one global δ13C carb stack, we selected alignments that allied the excursions that field workers have established (or speculated) are the Marinoan cap carbonate excursion, the Shuram excursion, and/or the basal Cambrian excursion. There are strengths and weaknesses to making explicit the temporal relationships (point-to-point correspondences) often left implicit in visual correlation. One strength is to extrapolate depositional ages by means of isotopic correlation, and here we explored this with a Bayesian Markov chain Monte Carlo age model that predicts a median age, and uncertainty, for every carbonate stratum in the global Ediacaran δ13C carb composite. Yet, one must caution against a false accuracy that can arise from selecting one alignment among many possibilities––the likelihood that time-uncertain time series can be stretched and squeezed into one unequivocal alignment is low. Thus, while these alignments are grounded in the expert assessment of the field worker, this global Ediacaran δ13C carb –Bayesian age model should be viewed as a working hypothesis to enrich, but not arbitrate, discussions of the correlation, synchrony, and completeness of Ediacaran successions. • Dynamic time warping yields a new Ediacaran global chronostratigraphic framework • Shuram carbon-isotope excursion average depletion and recovery durations constrained to 2.06 ± 1.87 Myr and 3.56 ± 1.92 Myr • A temporal gap is identified in the Ediacaran global δ13C carb record coeval with the Gaskiers glaciation and unconformities [ABSTRACT FROM AUTHOR]

This study investigated the relationship between geographic availability (and quality) of local early childhood education and care services and children's early mental health outcomes for all children entering their first year of full-time school in Melbourne, Australia. We capitalise on a unique population linked dataset, the Australian Early Development Census – Built Environment, which combines geospatial measures of children's neighbourhoods with demographic information and child mental health outcomes for all school entrants in Australia's 21 most populous cities and towns. Objective early childhood education and care service location and quality exposures were developed for each study child based on home addresses. Four geographic availability exposures (counts within 3 km) were examined for cross-sectional associations with child mental health outcomes (externalising and internalising difficulties, competence). We estimated associations using multilevel logistic regression (Markov Chain Monte Carlo estimation) adjusting for child demographics and stratifying by urbanicity. Children with higher counts of high-quality preschool services within 3 km of home had lower odds of difficulties and higher odds of competence. Overall, exposures were most consistently associated with children's competence. Across all outcomes, the most consistent patterning was observed for children living in the inner city and middle ring. Results varied depending on whether service quality was accounted for in measures of availability. Geographic availability of early childhood services showed patterning by neighbourhood disadvantage and by maternal education. We found some evidence that geographic availability of high-quality preschools was associated with better child mental health outcomes, but results varied by urbanicity. While future research is required to unpack these differences, these findings indicate the importance of accounting for both geographic availability and service quality simultaneously in future research, policy and practice. • High-quality early childhood services are critical for good early child development. • It is not known how availability of these services relates to child mental health. • We analysed linked geospatial and health data for all school entrants in Melbourne. • Greater availability of high-quality preschools was linked to better mental health. • Disadvantaged children had poorer access to high-quality services than their peers. [ABSTRACT FROM AUTHOR]

This paper investigates the heterogeneous impact of transportation infrastructure on trade-embodied CO 2 emission (TEC) transfer. First, a multi-scale input-output (MSIO) model is developed to analyze the TEC from 31 Chinese provinces to 64 Belt and Road Initiative (BRI) countries. Then, the panel quantile regression and Markov Chain Monte Carlo optimization methods are applied to estimate the impact of transportation infrastructure on TEC, taking into account the provincial heterogeneity. The main results are as follows: (1) The TEC of China's exports to BRI countries amounted to 311.01 Mt CO 2 annually from 2012 to 2016, with significant variations in TEC characteristics among different Chinese provinces. (2) China's transportation infrastructure network density (TID) predominantly exhibited a negative impact on exported TEC across various quantiles. Specifically, a 1% increase in TID was associated with a decrease in TEC ranging from approximately 0.25% to 3.99%. (3) As quantiles increased, the impact of transportation-converted turnover in China on TEC also rose, with a range of 0.66% to 0.94%. This study identifies the crucial regions and sectors of TEC from a 'provincial-national-global' multi-scale perspective, and implies that strengthening the construction of transportation infrastructure may improve the marginal effect of industrial structure transformation and environmental improvement brought about by trade activity. [Display omitted] • The 'provincial-national-global' multi-scale input-output model is established. • This study identifies the crucial regions and sectors of the trade-embodied CO 2. • Nonlinear nexus between trade-embodied CO 2 and transportation infrastructure is analyzed. • Heterogeneity manifested in exported trade-embodied CO 2 of Chinese provinces. • Transportation infrastructure has greater action on trade-embodied CO 2 in 75-90th quantiles. [ABSTRACT FROM AUTHOR]

The generation of synthetic households is challenging due to the necessity of maintaining consistency between the two layers of interest: the household itself, and the individuals composing it. Hence, the problem is typically tackled in two steps, first focusing on the individual layer and then on the household layer. The existing two-step simulation method proposes generating the households based on their roles which diminishes the generality of the approach and makes it difficult to reproduce despite its beneficial properties. In this paper, we propose an alternative extension of Gibbs sampling for generating hierarchical datasets such as synthetic households, in order to make simulation more general and reusable. We demonstrate the performance of our method in a case study based on the 2015 Swiss micro-census data and compare it against state-of-the-art approaches. We show the influence of modeling decisions on different performance metrics and how the analyst can easily enforce consistency while avoiding generating illogical households. We show that the algorithm maintains the conditional distributions while satisfying the marginals of all variables simultaneously, all while generating consistent synthetic households. • We propose an alternative practical extension of Gibbs sampling for generating hierarchical datasets such as synthetic households. • By grouping all relevant variables at all levels into a single random vector and sorting individuals by decreasing age, we can generate realistically detailed synthetic populations. • Implementing a separate Gibbs sampler for each household size accelerates the generation process and can be viewed as a variance reduction technique. • We demonstrate how modeling decisions impact various performance metrics and how analysts can ensure consistency while avoiding the creation of illogical households. • The results indicate that model-based methods are superior to data-driven approaches in controlling the generation process, thereby preventing the creation of illogical households. [ABSTRACT FROM AUTHOR]