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Since the beginning of the 21st Century obesity has become a major public health concern in Mexico. Survey data have been key to tracking the evolution of the national and regional prevalence of obesity over time. However, these data are insufficient for policymakers and researchers interested in obesity from a more local and spatial perspective. This paper uses two secondary data sources: the Mexican National Health and Nutrition Survey 2021 and the Mexican National Population Census 2020. This paper implements a Bayesian hierarchical approach to model survey and census data to produce municipal-level estimates for Mexico in 2020. The results indicate that obesity has inter and intra-regional variability. Obesity is more prevalent in the north and in the Yucatan peninsula and tends to be lower in the state of Chiapas. However, within these regions there is some degree of variability in obesity rates. The results provide a more detailed geographical picture of obesity across Mexico and raise the possibility of using the resulting estimates for further statistical and policy-relevant research. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to a Bayesian accelerated life testing model is presented. The Weibull distribution is used as the life distribution and the generalized Eyring model as the time transformation function. This is a model that allows for the use of more than one stressor, whereas other commonly used acceleration models, such as the Arrhenius and power law models, incorporate one stressor. The use of the generalized Eyring‐Weibull model developed in this paper is demonstrated in a case study, where Markov chain Monte Carlo methods are utilized to generate samples for posterior inference. [ABSTRACT FROM AUTHOR]

Commonly, it is accepted that oncology treatment would yield outcomes with a certain determinism without any quantitative support or mathematical model that establishes such determinations. Nowadays, with the advent of nanomedicine, the targeting drug delivery scheme has emerged, whose central objective is the uptake of nanoparticles by tumors. Once they are injected into the bloodstream, it is unclear as to which process governs the directing of nanoparticles towards the desired target, deterministic or stochastic. In any scenario, an optimal outcome, small toxicity and minimal dispersion of drugs is expected. Commonly, it is expected that an important fraction of them can be internalized into tumor. In this manner, due to the fraction of nanoparticles that have failed to uptake, the success of the drug delivery scheme might be at risk. In this paper, a theory based on the sequence electrodynamics–diffusion–Bayes theorem is presented. The Bayesian probability that emerges at the end of the sequence might be telling us that dynamical processes based on the injection of electrically charged nanoparticles might be dictated by stochastic formalism. Thus, rather than expecting a deterministic process, the chain of events would convert the drug delivery scheme to be dependent on a sequence of conditional probabilities. [ABSTRACT FROM AUTHOR]

Incidents are caused by a variety of factors, and there are correlations between incident causative factors. How to effectively clarify the importance of incidental injury-causing factors and their correlations is the current technical challenge in the field of incident causation analysis. This paper takes the study of injury-causing factors and their relationships between object-striking incidents in the process of construction as an example, and it statistically analyzes the incident investigation reports of 126 cases of object-striking incidents in construction projects in China from 2016 to 2022; it screens out 52 categories of incident-causing factors. The Apriori algorithm and FP-growth algorithm are used to data mine the influencing factors obtained from the 126 object-striking incidents: 28 main incident causative items of object-striking incidents and the respective correlation degree between each factor are obtained. By analyzing the support degree of the main incident causation items, as well as comparing and analyzing the results of the incident causation support degree and association rules with Bayesian inference, 9 key injury-causing factors of object-striking incidents are identified. The research results put forward a new research idea for the analysis of the injury factors of object-striking incidents in construction, which can provide theoretical reference for improving the pertinence and effectiveness of incident prevention measures. [ABSTRACT FROM AUTHOR]

Brown's 1971 paper "Admissible estimators, recurrent diffusions and insoluble boundary value problems" is a landmark in the admissibility literature. It nearly completely settles the issue of admissibility/inadmissibility for estimating the mean of a multivariate normal distribution with identity covariance under sum of squared error loss. We revisit this wonderful tour de force on its 50th anniversary and present an alternative and more direct proof of the result for generalized Bayes estimators corresponding to priors which are a subclass of scale mixtures of spherical normals. • Brown's 1971 paper is revisited on the 50th anniversary. • The issue of admissibility/inadmissibility for estimating the mean of a multivariate normal distribution is treated. • More direct and self-contained proofs for admissibility/inadmissibility are provided. [ABSTRACT FROM AUTHOR]

In this paper a new bivariate lifetime distribution is introduced. Its marginal distribution functions follow two-parameter Chen distribution, which has a bathtub shaped or increasing hazard rate functions. The proposed distribution, which we call a bivariate Chen distribution (BCD), is of Marshall-Olkin type and it is a singular distribution. Several properties of this proposed distribution are discussed. The BCD distribution has four unknown parameters. The maximum likelihood (ML) method and the Bayes techniques are used to estimate the unknown parameters. The maximum likelihood estimators or the Bayes estimators cannot be obtained in closed form. Numerical methods have been used in both cases. A real data set is analyzed using the proposed distribution for illustrative and comparison purposes. An application to dependent competing risks data is discussed, and finally we have extended the BCD to the multivariate case. [ABSTRACT FROM AUTHOR]

In 2017, D. Horsman, C. Heunen, M. Pusey, J. Barrett and R. Spekkens proved that there is no physically reasonable assignment that takes a quantum channel and an initial state and produces a joint state on the tensor product of the input and output spaces. The interpretation was that there is a clear distinction between space and time in the quantum setting that is not visible classically, where in the latter, one can freely use Bayes' theorem to go between joint states and marginals with noisy channels. In this paper, we prove that there actually is such a physically reasonable assignment, bypassing the no-go result of Horsman et al., and we illustrate that this is achievable by restricting the domain of their assignment to a domain which represents the given data more faithfully. [ABSTRACT FROM AUTHOR]

Ground-Based Synthetic Aperture Radar (GB-SAR) enables to monitor a scene in a distance up to a few km and with an accuracy of around one mm. It is mainly used for landslide monitoring but also used to monitor man-made structures such as bridges and dams. Due to the particular distance-dependent imaging concept and related geometric distortion effects, the interpretation of the SAR images can be difficult, especially in the case of complex man-made structures. The geocoding of the SAR data enables to project the 2D SAR image on a 3D digital elevation model (DEM) or a 3D terrain model and highly improves the interpretation of the observed deformation. It also allows to fuse SAR-based observations with other data sources. The purpose of this paper is to present a new algorithm to geocode GB-SAR data in complex scenarios. The method introduces two new components, a Bayesian statistical approach and a ray-tracing algorithm. The first one uses an a priori function on the received intensity to improve the geocoding in case of strong layover, while the latter considers areas in the radar shadow to be excluded from geocoding. Moreover, ray-tracing provides the starting point for the analysis of signals with multiple reflections which are also discussed in this paper. Our algorithm is applied to and evaluated with two case studies. The first one demonstrates the improved geocoding of GB-SAR data on the Linach dam in the Black Forest in Germany, which is characterized by a rather complex architecture. The second case study shows the application of the method at the Enguri dam in Georgia, which is characterized by its large dimensions and a dam height over 270 m. [ABSTRACT FROM AUTHOR]

• Here we demonstrate the evaluation of forensic evidence using a real life case of an alleged sexual assault. • We have used the European Network of Forensic Science Institutes (ENFSI) guideline for evaluative reporting in forensic science. • We show evaluation is possible even when additional information is provided and some or all results are known. This paper demonstrates a logical framework for evaluating forensic evidence, first described by Cook et al. [1,2], using a casework example of an alleged sexual assault involving semen transfer. Here we show in real time how the case strategy can change with additional information and how to use available experience and published data to interpret the findings obtained, given the background information provided. The findings of the case are interpreted using the Bayesian approach and are reported by giving the strength of support of scientific findings for one proposition rather than a competing proposition, as per the European Network of Forensic Science Institutes (ENSFI) guideline for evaluative reporting. We believe that using this paper as a template will aid other Forensic Science Practitioners (FSP) to add value and weight to their work by assisting them in evaluating and interpreting their own findings. [ABSTRACT FROM AUTHOR]

Air pollution is a serious issue that currently affects many industrial cities in the world and can cause severe illness to the population. In particular, it has been proven that extreme high levels of airborne contaminants have dangerous short‐term effects on human health, in terms of increased hospital admissions for cardiovascular and respiratory diseases and increased mortality risk. For these reasons, an accurate estimation of airborne pollutant concentrations is crucial. In this paper, we propose a flexible novel approach to model hourly measurements of fine particulate matter and meteorological data collected in Beijing in 2014. We show that the standard state space model, based on Gaussian assumptions, does not correctly capture the time dynamics of the observations. Therefore, we propose a non‐linear non‐Gaussian state space model where both the observation and the state equations are defined by copula specifications, and we perform Bayesian inference using the Hamiltonian Monte Carlo method. The proposed copula state space approach is very flexible, since it allows us to separately model the marginal distributions and to accommodate a wide variety of dependence structures in the data dynamics. We show that the proposed approach allows us not only to accurately estimate particulate matter measurements, but also to capture unusual high levels of air pollution, which were not detected by measured effects. [ABSTRACT FROM AUTHOR]

It is necessary to develop new drilling and breaking technology for hard rock construction. However, the process of high-voltage electro-pulse (HVEP) rock-breaking is complex, and the selection of electro-pulse boring (EPB) process parameters lacks a theoretical basis. Firstly, the RLC model, TV-RLC model, and TV-CRLC model are established based on the characteristics of the HVEP circuit to predict the EPB dynamic discharge curve. Secondly, the parameters are identified by the Particle Swarm Optimization Genetic Algorithm (PSO-GA). Finally, due to the nonlinear and complex time-varying characteristics of the discharge circuit, the discharge circuit prediction models based on Bayesian fusion and current residual normalization fusion method are proposed, and the optimal weight of these three models is determined. Compared with the single models for EPB current prediction, the average relative error reduction rates based on Bayesian fusion and current residual normalization fusion methods are 25.5% and 9.5%, respectively. In this paper, the discharge circuit prediction model based on Bayesian fusion is established, which improves the prediction accuracy and reliability of the model, and it guides the selection of process parameters and the design of pulse power supply and electrode bits. [ABSTRACT FROM AUTHOR]

The transfer capabilities of High-Voltage Overhead Lines (HV OHLs) are often limited by the critical power line temperature that depends on the magnitude of the transferred current and the ambient conditions, i.e., ambient temperature, wind, etc. To utilize existing power lines more effectively (with a view to progressive decarbonization) and more safely with respect to the critical power line temperatures, this paper proposes a Dynamic Thermal Rating (DTR) approach using IoT sensors installed on some HV OHLs located in different Italian geographical locations. The goal is to estimate the OHL conductor temperature and ampacity, using a data-driven thermo-mechanical model with the Bayesian probability approach, in order to improve the confidence interval of the results. This work highlights that it could be possible to estimate a space-time distribution of temperature for each OHL and an increase in the actual current threshold values for optimizing OHL ampacity. The proposed model is validated using the Monte Carlo method. [ABSTRACT FROM AUTHOR]

In this paper, an accelerated life testing model using the Birnbaum‐Saunders distribution and the generalized Eyring model is explored. A Bayesian approach is proposed, considering the combined effect of one thermal stressor and one non‐thermal stressor. The model is demonstrated via an application to an electronics epoxy packaging ALT data set, with temperature and humidity as accelerated stressors. Markov chain Monte Carlo methods are employed to generate posterior samples for inference. A sensitivity analysis is performed via the deviance information criterion. The predictive reliability is also investigated. [ABSTRACT FROM AUTHOR]

In this paper, I propose and defend a pair of necessary conditions on evidence-based knowledge which bear resemblance to the troubled sensitivity principles defended in the philosophical literature (notably by Fred Dretske). We can think of the traditional principles as simple but inaccurate approximations of the new proposals. Insofar as the old principles are intuitive and used in scientific and philosophical contexts, but are plausibly false, there's a real need to develop precise and correct formulations. These new renditions turned out to be more cautious, so they won't be able to do everything the old principled promised they could. For example, they respect closure for knowledge. But these sober formulations, or something like them, might be the best that we can do with respect to sensitivity. And there's value in understanding the limits to these types of principles. [ABSTRACT FROM AUTHOR]

Oil price forecasting is one of the most challenging issues since it is noisy, non-stationary, and chaotic. In this paper, we design a Bayesian Nonlinear Quantile method consisting of a Threshold Improved model and an Adaptive MCMC model to improve predicting performance. Specifically, the threshold improve model is introduced to solve the problems caused by the completely asymmetric distribution, and the Adaptive MCMC model is used to get the optimal threshold. Besides, the two-stage framework is applied to improve traditional methods' performance, including the Indirect GARCH model and Asymmetric Slope model. The experimental results show that our approach provides a promising alternative to oil price prediction, and the framework also improve the performance of the traditional methods. The contribution of this paper is to improve the accuracy of the oil price forecasting model, and the framework applies to other energy prices as well. [ABSTRACT FROM AUTHOR]

Appropriate levels of arousal potential induce hedonic responses (i.e., emotional valence). However, the relationship between arousal potential and its factors (e.g., novelty, complexity, and uncertainty) have not been formalized. This paper proposes a mathematical model that explains emotional arousal using minimized free energy to represent information content processed in the brain after sensory stimuli are perceived and recognized (i.e., sensory surprisal). This work mathematically demonstrates that sensory surprisal represents the summation of information from novelty and uncertainty, and that the uncertainty converges to perceived complexity with sufficient sampling from a stimulus source. Novelty, uncertainty, and complexity all act as collative properties that form arousal potential. Analysis using a Gaussian generative model shows that the free energy is formed as a quadratic function of prediction errors based on the difference between prior expectation and peak of likelihood. The model predicts two interaction effects on free energy: that between prediction error and prior uncertainty (i.e., prior variance) and that between prediction error and sensory variance. A discussion on the potential of free energy as a mathematical principle is presented to explain emotion initiators. The model provides a general mathematical framework for understanding and predicting the emotions caused by novelty, uncertainty, and complexity. The mathematical model of arousal can help predict acceptable novelty and complexity based on a target population under different uncertainty levels mitigated by prior knowledge and experience. [ABSTRACT FROM AUTHOR]

The paper introduces, compares and contrasts formal models of source reliability proposed in the epistemology literature, in particular the prominent models of Bovens and Hartmann (Bayesian epistemology, Oxford University Press, Oxford, 2003) and Olsson (Episteme 8(02):127–143, 2011). All are Bayesian models seeking to provide normative guidance, yet they differ subtly in assumptions and resulting behavior. Models are evaluated both on conceptual grounds and through simulations, and the relationship between models is clarified. The simulations both show surprising similarities and highlight relevant differences between these models. Most importantly, however, our evaluations reveal that important normative concerns arguably remain unresolved. The philosophical implications of this for testimony are discussed. [ABSTRACT FROM AUTHOR]

Intelligent prediction of rock bursts has great significance in rock mechanics research and a high value in engineering applications. An intelligent rockburst prediction method based on a Bayes-optimized convolutional neural network (BOCNN) was proposed. First, an exploratory analysis of data was conducted using joint distribution diagrams and the heat map of the correlation matrix to establish a high-quality data set of rockburst engineering cases and a parameter system for rockburst prediction. Second, six rockburst prediction models were built by combining machine learning algorithms, such as random forest, k-nearest neighbor (KNN), and Bayes, deep learning (CNN1d and CNN2d), and BOCNN. In addition, we used accuracy, precision, recall, F1 score, receiver operating characteristic curve, Taylor diagram, and the probability indicator of prediction results as indicators to evaluate the accuracy of the models. A comparative analysis of the six rockburst prediction models was conducted to explore rockburst prediction models with good robustness, generalization performance, and high accuracy. Moreover, 11 comparative models were established for comparative analysis. Then, we used the MATLAB tool to build an intelligent rockburst prediction system and applied the findings to the Jiangbian Hydropower Station in Sichuan Province, China. The results of the study show that the intelligent rockburst prediction system can provide technical support for predicting rockburst hazards in mining, transportation, and water conservancy and hydropower projects and a scientific basis for later construction and the design of support structures. [ABSTRACT FROM AUTHOR]

Oil price forecasting is one of the most challenging issues since it is noisy, non-stationary, and chaotic. In this paper, we design a Bayesian Nonlinear Quantile method consisting of a Threshold Improved model and an Adaptive MCMC model to improve predicting performance. Specifically, the threshold improve model is introduced to solve the problems caused by the completely asymmetric distribution, and the Adaptive MCMC model is used to get the optimal threshold. Besides, the two-stage framework is applied to improve traditional methods' performance, including the Indirect GARCH model and Asymmetric Slope model. The experimental results show that our approach provides a promising alternative to oil price prediction, and the framework also improve the performance of the traditional methods. The contribution of this paper is to improve the accuracy of the oil price forecasting model, and the framework applies to other energy prices as well.

Bayesian approaches have long been a small minority group in scientific practice, but quickly acquired a high level of popularity since the 1990s. This paper shall describe and analyze this turn. I argue that the success of Bayesian approaches hinges on computational methods that make a class of models predictive that would otherwise lack practical relevance. Philosophically, however, this orientation toward prediction comes at a price. The new computational approaches change Bayesian rationality in an important way. Namely, they undercut the interpretation of priors, turning them from an expression of beliefs held prior to new evidence into an adjustable parameter that can be manipulated flexibly by computational machinery. Thus, in the case of Bayes, one can see a coevolution of computing technology, an exploratory–iterative mode of prediction, and the conception of rationality. [ABSTRACT FROM AUTHOR]

Previous studies have indicated and shown the feasibility of retrieving snow density from ground based passive microwave measurements at the L band (1 – 2 GHz) from theoretical and experimental viewpoints. This paper expands the previous studies by presenting a case study of the retrieval problem with space borne brightness temperature measurements from the SMOS satellite over Sodankylä, Finland. To successfully retrieve snow density, also ground and forest parameters were included to the retrieval process. The retrieved variables were validated against in-situ ground, snow, and forest measurements made around the Sodankylä area over 12 winters from 2010 to 2022. A Bayesian framework was used to account for and quantify the uncertainties associated with the retrieval process. The results show good agreement between the retrieved ground and forest variables with the respective reference values. The snow density retrievals were seen to suffer from multiple sources of geophysical noise. However, the monthly average bulk snow density was successfully retrieved under stable midwinter conditions. The best agreement with the in-situ measurements was found for February, with a bias of -0.4 kg/m3, an unbiased root mean square difference of 12.2 kg/m3, and a correlation coefficient of 0.75. • Ground, snow, and forest parameters were retrieved over 12 winters. • The results were validated against comprehensive in-situ measurements. • The study site represents a typical heterogeneous boreal forest site. • Retrieved vegetation optical depth follows the modelled dynamics and value range. • Retrieved snow density agrees with measurements during the cold mid-winter period. [ABSTRACT FROM AUTHOR]

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment. [ABSTRACT FROM AUTHOR]

• Use principal component analysis to extract the comprehensive displacement law of multiple monitoring points. • The BEAST algorithm is introduced to decompose the displacement into two parts: seasonal and trend, and quantify its change in different periods. • Based on the segmented quantitative analysis model, the evolution mechanism of dam deformation law is quantitatively analyzed. • The advantages and disadvantages of correlation time series decomposition algorithm and its applicability are discussed. The dam and its operating environment are complex and uncertain and often show many different operating rules during its operating period. In this paper, firstly, the principal component analysis is used to extract the comprehensive displacement of multiple measuring points; Then, the comprehensive displacement is decomposed into seasonal and trend parts by using the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) method, and the change of its change law is quantitatively analyzed; Finally, the influence of each environmental quantity on the dam displacement is quantified by selecting the change point reasonably. The method proposed in this paper is verified by the quantitative analysis of the displacement law of multiple measuring points of an earth rock dam in different periods. The research results provide strong technical support for better analysis of the evolution process of dam deformation behavior and quantitative interpretation of dam deformation mechanism. [ABSTRACT FROM AUTHOR]

• Variational Laplace (VL) is a scheme for Bayesian modelling. • VL is widely used in neuroimaging, in particular DCM. • This paper provides a tutorial explanation of the math and algorithms. • New standalone code is provided to enable re-implementation. • The supplementary materials provide worked derivations. This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago. Variational Laplace (VL) provides a generic approach to fitting linear or non-linear models, which may be static or dynamic, returning a posterior probability density over the model parameters and an approximation of log model evidence, which enables Bayesian model comparison. VL applies variational Bayesian inference in conjunction with quadratic or Laplace approximations of the evidence lower bound (free energy). Importantly, update equations do not need to be derived for each model under consideration, providing a general method for fitting a broad class of models. This primer is intended for experimenters and modellers who may wish to fit models to data using variational Bayesian methods, without assuming previous experience of variational Bayes or machine learning. Accompanying code demonstrates how to fit different kinds of model using the reference implementation of the VL scheme in the open-source Statistical Parametric Mapping (SPM) software package. In addition, we provide a standalone software function that does not require SPM, in order to ease translation to other fields, together with detailed pseudocode. Finally, the supplementary materials provide worked derivations of the key equations. [ABSTRACT FROM AUTHOR]

A unique feature of Bayesian estimation is the inclusion of prior knowledge through prior distributions. These prior distributions can benefit or impair many components of the ensuing analysis. Priors are especially important to assess in the context of structural equation models (SEMs), which often carry data and modeling complexities where priors can be particularly influential. In this article, we illustrate a statistical approach to assess the impact of our prior specifications: the prior predictive checking procedure. We introduce a comprehensive prior predictive checking workflow that organizes the procedure into clear steps, and we relate this workflow to the SEM framework. Through three examples, we demonstrate how the workflow can aid researchers in more fully understanding different aspects of their prior specification within SEM. Code and additional resources are provided in the online to facilitate future application of the prior predictive checking procedure within the SEM framework. [ABSTRACT FROM AUTHOR]

We propose a neural network-based approach to calculate the value of a chess square–piece combination. Our model takes a triplet (color, piece, square) as the input and calculates a value that measures the advantage/disadvantage of having this piece on this square. Our methods build on recent advances in chess AI, and can accurately assess the worth of positions in a game of chess. The conventional approach assigns fixed values to pieces (= ∞, = 9, = 5, = 3, = 3, = 1). We enhance this analysis by introducing marginal valuations. We use deep Q-learning to estimate the parameters of our model. We demonstrate our method by examining the positioning of knights and bishops, and also provide valuable insights into the valuation of pawns. Finally, we conclude by suggesting potential avenues for future research. [ABSTRACT FROM AUTHOR]

The role of digital transformation in creating value for commercial banks has been interesting to researchers for a long time. While many commercial banks have significantly investigated digital transformation, researchers and managers have still met many difficulties examining the distribution of digital transformation to business performance. This paper aims to evaluate the impact of digital transformation on Vietnamese commercial banks' performance by different sizes, from there proposing policy implications of digital transformation to improve the banking performance. To achieve this goal, we used a quantitative research method. Specifically, we applied the GMM system (SGMM) of Blundell and Bond for the data of 13 joint-stock commercial banks in Vietnam in the period from 2011 to 2019. Then Bayesian analysis is performed to test the robustness of the models estimated by the SGMM method. The result shows that the digital transformation has a positive impact on the performance of Vietnamese commercial banks. Besides, we also find that the larger the banks, the greater the positive impact of digital transformation on bank performance. Therefore, the efficiency of digital transformation depends on a bank scale. [ABSTRACT FROM AUTHOR]

Dr. Leonhard presents a comprehensive and insightful critique of the existing malingering research literature and its implications for neuropsychological practice. Their statistical critique primarily focuses on the crucial issue of diagnostic inference when multiple tests are involved. While Leonhard effectively addresses certain misunderstandings, there are some overlooked misconceptions within the literature and a few new confusions were introduced. In order to provide a balanced commentary, this evaluation considers both Leonhard's critiques and the malingering research literature. Furthermore, a concise introduction to Bayesian diagnostic inference, utilizing the results of multiple tests, is provided. Misunderstandings regarding Bayesian inference are clarified, and a valid approach to Bayesian inference is elucidated. The assumptions underlying the simple Bayes model are thoroughly discussed, and it is demonstrated that the chained likelihood ratios method is an inappropriate application of this model due to one reason identified by Leonhard and another reason that has not been previously recognized. Leonhard's conclusions regarding the primary dependence of incremental validity on unconditional correlations and the alleged mathematical incorrectness of the simple Bayes model are refuted. Finally, potential directions for future research and practice in this field are explored and discussed. [ABSTRACT FROM AUTHOR]

Objective: This study compared and explored the neurocognitive profiles of siblings of persons with and without neurodevelopmental conditions (NDCs) and associations between objective test performance and self-reported psychosocial functioning. Methods: Siblings of persons with and without NDCs (64 NDC and 64 control siblings; mean age 19.88 years, range 11-27 years, 73.44% female, 75.78% White Caucasian) completed self-report questionnaires and self-administered computerized neurocognitive tests of executive functioning (EF). Using Bayesian analyses, we examined cross-sectional associations between self-reported psychosocial functioning and cognitive test performance, and predictors of EF over 15 months. Results: NDC siblings had poorer working memory, inhibition, attention, and shifting compared to controls, as measured by experimental paradigms on the backward Corsi span, N-Back 2-back task, Stop Signal Task, Sustained Attention to Response Task, and the Wisconsin Card Sorting Test (effect size δ ranging 0.49 to 0.64). Bayesian cross-sectional networks revealed negative emotion reactivity and working memory difficulties were central to the NDC sibling network. Over 15 months, poorer EF (k low test scores) was predicted by negative emotion reactivity, sleep problems, and anxiety, over and above effects of age and subclinical autistic and ADHD traits. Siblings of autistic individuals and persons with fetal alcohol spectrum disorder had higher rates of neurocognitive and psychiatric difficulties than other NDCs and controls (Bayes factors >20). Conclusions: Neurocognitive difficulties were associated with transdiagnostic vulnerability to poorer wellbeing in NDC siblings. These findings demonstrate the feasibility of remote online cognitive testing and highlight the importance of individualized prevention and intervention for NDC siblings. [ABSTRACT FROM AUTHOR]

The commentators of our target article present several detailed arguments to refute the opposing theory. The real issue, however, seems to be the fundamental question of how the mind represents the content of beliefs. We distinguish between qualitative, quantitative and comparative approaches to modeling uncertain beliefs. We describe which theory falls into which of these classes. We also argue that the comparative level is the most fundamental, and challenge commentators to justify why they think that beliefs have more or less structure in the human mind than can be captured at the comparative level. [ABSTRACT FROM AUTHOR]

Transporting water to supply livestock is one of the great challenges of the drylands. Ranchers usually make impoundments, filled by runoff, to access freshwater for cattle supply in flat rangelands. The aim of this study was to understand rainfall-runoff generation and water storage temporal dynamics of impoundments in the Dry Chaco rangelands (Argentina). Thus, we instrumented six impoundments over three consecutive years and analyzed water storage data by developing a probabilistic model. For all impoundments, the rainfall event size thresholds to generate runoff presented values between 15 and 33 mm. Once they reached this threshold, the water gain response slopes presented values between 19 and 99 m3 mm−1. Loss patterns of water storage were described by exponential or linear functions. The predicted water storage dynamics presented high accuracy with the observed time series for all impoundments (RMSD between 380 and 1320 m3). The model only needs daily rainfall and air temperature to be run, making it easy to be used by scientists, ranchers, or local decision makers. It may be used to explore the hydrological functioning of small and seasonal water bodies of different sites of the world exposed to drought episodes caused by high climate variability and/or climate change. [ABSTRACT FROM AUTHOR]

We provide an applied introduction to Bayesian estimation methods for empirical accounting research. To showcase the methods, we compare and contrast the estimation of accruals models via a Bayesian approach with the literature's standard approach. The standard approach takes a given model of normal accruals for granted and neglects any uncertainty about the model and its parameters. By contrast, our Bayesian approach allows incorporating parameter and model uncertainty into the estimation of normal accruals. This approach can increase power and reduce false positives in tests for opportunistic earnings management as a result of better estimates of normal accruals and more robust inferences. We advocate the greater use of Bayesian methods in accounting research, especially since they can now be easily implemented in popular statistical software packages. [ABSTRACT FROM AUTHOR]

Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users' indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%. [ABSTRACT FROM AUTHOR]

In the article, we exploit the Pearson system of distributions to obtain expressions for the posterior mean and posterior variance in Tweedie's formula. This has interesting consequences in the study of micro-array data where the interest is uncovering unusual genes. Another application deals with the development of new non-parametric tests for the two sample problems of location and of scale. These tests are based on an approximation of the locally optimal score test statistics. [ABSTRACT FROM AUTHOR]

The performance of age estimation methods may vary due to a combination of method- and sample-related factors. Method development and evaluation necessitates understanding what influences these factors have on age estimation outcomes. In the specific context of juvenile dental age estimation, we used a single dataset and complete factorial design to systematically test four potential sources of difference: age distributions of reference and target sample (uniform, unimodal, U-shaped), Bayesian (multivariate Bayesian cumulative probit) vs. classical regression modeling (multivariate adaptive regression splines i.e. MARS), and model selection bias. The dataset consisted of 850 sets of left mandibular molar scores from London children 5–18 years old. True age and estimated age intervals in target samples were compared for bias, root-mean-squared error, precision, and accuracy using locally weighted smoothing of performance measures across the age range and means of performance metrics between factor-level combinations. We found interactions of model type, reference distribution, and target distribution. MARS models showed consistent evidence of age mimicry. Central tendency of the reference sample corresponded with increased bias while central tendency of the target sample corresponded with reduced RMSE and reduced precision for both model types. We found evidence of model selection bias, mitigated through averaging model metrics. We conclude that reference and target sample distribution influences and model selection bias are sufficient to cause difference in model performance within a single population. We suggest using Bayesian modeling, drawing uniform reference and target samples, and calculating test error on a hold-out sample to mitigate these challenges in method development. [ABSTRACT FROM AUTHOR]

Objectives: the availability of smartphone-based mindfulness training applications (apps) may circumvent many barriers to receiving in-person help, but little controlled research has been conducted on them. This study sought to evaluate the effectiveness of a widely used -mindfulness training app, Headspace, at reducing anxiety and worry. Methods: this study used a randomized-controlled design to examine the app using a 3 (Time; baseline, 4 weeks, 8 weeks) × 2 (Access; immediate, delayed for 4 weeks) design. Participants who reported moderate to high anxiety or worry were randomly assigned to receive -either -immediate access or delayed access to the app. For null hypothesis significance testing (NHST), analyses of variance were used to test the hypotheses that app access for 4 and 8 weeks would reduce anxiety and worry as compared to waitlist or baseline and that app access for 8 weeks would reduce anxiety and worry as compared to 4 weeks. Bayes estimates were used to -determine the level of evidence for the hypothesis that app access reduces anxiety and worry. Results: four weeks of app access significantly reduced anxiety symptoms, as did 8 weeks, but NHST indicated there were no significant difference between 4 and 8 weeks of access. We failed to reject the null for the analysis of variance on worry, but Bayesian estimates indicated substantial evidence for the hypothesis that the mindfulness training app reduces worry. Conclusions: this research shows that using Headspace can reduce anxiety and worry, but that there does not appear to be a consistent dose relation. [ABSTRACT FROM AUTHOR]

The distance to failure of the upper crustal rock in the prevalent stress field is of importance to better understand fault reactivation by natural and induced processes as well as to plan and manage georeservoirs. In particular, the contemporary stress state is one of the key ingredients for this assessment. To provide a continuous description of the 3D absolute stress state geomechanical‐numerical models are used. However, stress magnitude data for model calibration are sparse and incomplete and thus, the resulting model uncertainties are large. In order to reduce the uncertainties, we incorporate additional constraints on stress magnitudes to check the plausibility of different data‐based stress states. We use formation integrity tests, borehole breakouts, drilling induced fractures, and observations of seismicity and distinct seismological quiescence. This information is weighted according to its confidence and the agreement with the different modeled stress states is assessed. The information is introduced to a Bayesian approach to estimate weights of the modeled stress states and thereby identify their plausibility. A case study in southern Germany shows the ability of the approach to identify from a wide range of stress states a small number of plausible ones and reject implausible stress states. This significantly reduces the number of stress states and thus lowers the model uncertainties. Plain Language Summary: The upper crust of the Earth (upper few kilometers) is subject to a kind of pressure, referred to as stress. When the stress becomes larger than the strength of the rock, the rock breaks. Sometimes this can be measured or even felt as a seismic event. It happens naturally but may also happen due to human activity. To prevent such induced seismic event, it is important to know the stress state. But there is only few information on the magnitude of the stress so we need computer models to predict the stress state. These models are often not very precise since there is only few information on the stress magnitudes and in addition they are often contradicting. We use all stress magnitude information individually to model various stress states. Then we look at other information that is related to the stress state but does not provide stress magnitude information on its own. We compare this information with the modeled stress states to find out whether a stress state agrees with the additional information or not. This allows us to identify a few realistic stress state models out of a wide range of possible ones. This reduces the uncertainties of the stress predictions. Key Points: Bayesian approach to uncertainty quantification and reduction of 3D geomechanical‐numerical models of the undisturbed stress stateAdditional constraints on the stress state by formation integrity tests, borehole breakouts, and drilling induced tensile fracturesConstraints on the differential stress by observation of seismicity or distinct seismological quiescence [ABSTRACT FROM AUTHOR]

Commonly, it is accepted that oncology treatment would yield outcomes with a certain determinism without any quantitative support or mathematical model that establishes such determinations. Nowadays, with the advent of nanomedicine, the targeting drug delivery scheme has emerged, whose central objective is the uptake of nanoparticles by tumors. Once they are injected into the bloodstream, it is unclear as to which process governs the directing of nanoparticles towards the desired target, deterministic or stochastic. In any scenario, an optimal outcome, small toxicity and minimal dispersion of drugs is expected. Commonly, it is expected that an important fraction of them can be internalized into tumor. In this manner, due to the fraction of nanoparticles that have failed to uptake, the success of the drug delivery scheme might be at risk. In this paper, a theory based on the sequence electrodynamics–diffusion–Bayes theorem is presented. The Bayesian probability that emerges at the end of the sequence might be telling us that dynamical processes based on the injection of electrically charged nanoparticles might be dictated by stochastic formalism. Thus, rather than expecting a deterministic process, the chain of events would convert the drug delivery scheme to be dependent on a sequence of conditional probabilities.

Background: The role of radiology in patients with clinical suspicion of COVID-19 is evolving with scientific evidence, but there are differences in opinion on when and how the technique should be used for clinical diagnosis. Purpose: To estimate the pre-test and post-test probability that a patient has COVID-19 in the event of a positive and/or negative result from chest X-ray and chest computed tomography (CT) radiological studies, comparing with those of real time polymerase chain reaction (RT-PCR) tests. Methods: The literature on the sensitivity and specificity of the chest X-ray, chest CT, and RT-PCR was reviewed. Based on these reported data, the likelihood ratios (LR) were estimated and the pre-test probabilities were related to the post-test probabilities after positive or negative results. Results: The chest X-ray has only a confirmatory value in cases of high suspicion. Chest CT analyses showed that when it is used as a general study, it has almost confirmatory value under high clinical suspicion. A chest CT classified with CO-RADS ≥ 4 has almost a diagnostic certainty of COVID-19 even with moderate or low clinical presumptions, and the CO-RADS 5 classification is almost pathognomonic before any clinical presumption. To rule out COVID-19 completely is only possible in very low clinical assumptions with negative RT-PCR and/or CT. Conclusions: Chest X-ray and especially CT are fast studies that have the capacity to report high probability of COVID-19, being a real contribution to the concept of "probable case" and allowing support to be installed in an early and timely manner. [ABSTRACT FROM AUTHOR]