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Despite increasing digitalization and automation, complex production processes often require human judgment/decision-making adaptability. Humans can abstract and transfer knowledge to new situations. People in production are an irreplaceable resource. This paper presents a new concept for digitizing human expertise and their ability to make knowledge-based decisions in the production area based on ontologies and causal Bayesian networks for further research. Dedicated approaches for the ontology-based creation of Bayesian networks exist in the literature. Therefore, we first comprehensively analyze previous studies and summarize the approaches. We then add the causal perspective, which has often not been an explicit subject of consideration. We see a research gap in the systematic and structured approach to ontology-based generation of causal graphs (CGs). At the current state of knowledge, the semantic understanding of a domain formalized in an ontology can contribute to developing a generic approach to derive a CG. The ontology functions as a knowledge base by formally representing knowledge and experience. Causal inference calculations can mathematically imitate the human decision-making process under uncertainty. Therefore, a systematic ontology-based approach to building a CG can allow digitizing the human ability to make decisions based on experience and knowledge. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network. This paper investigated the differences between conference papers and journal papers in the field of computer science based on Bayesian network, a knowledge-representative framework that can model relationships among all variables in the network. We defined the variables required for Bayesian networks modeling, calculated the values of each variable based Aminer dataset (a literature data set in the field of computer science), learned the Bayesian network and derived some findings based on network inference. The study found that conferences are more attractive to senior scholars, the academic impact of conference papers is slightly higher than journal papers, and it is uncertain whether conference papers are more innovative than journal papers. The study was limited to the field of computer science and employed Aminer dataset as the sample. Further studies involving more diverse datasets and different fields could provide a more complete picture of the matter. By demonstrating that Bayesian networks can effectively analyze issues in Scientometrics, the study offers valuable insights that may enhance researchers' understanding of the differences between journal and conference in computer science. Academic conferences play a crucial role in facilitating scholarly exchange and knowledge dissemination within the field of computer science. Several studies have been conducted to examine the distinctions between conference papers and journal papers in terms of various factors, such as authors, citations, h-index and others. Those studies were carried out from different (independent) perspectives, lacking a systematic examination of the connections and interactions between multiple perspectives. This paper supplements this deficiency based on Bayesian network modeling. [ABSTRACT FROM AUTHOR]

There is a limited body of experimental research examining the comparability of completing self-report surveys using different computerized devices. Additionally, available literature has not used complete or optimal procedures for determining device equivalence. The current study examined the comparability of surveys completed using paper-and-pencil and three popular devices: smartphone, tablet, and desktop computer. Participants consisted of 211 college students randomly assigned to conditions who completed measures of personality, social desirability, and computer self-efficacy. Results showed evidence of qualitative equivalence (internal consistency and subscale intercorrelations) across conditions. For quantitative and auxiliary equivalence, both equivalence testing and Bayesian analyses were conducted. Equivalence testing indicated quantitative (mean score) equivalence, as well as comparability for one aspect of auxiliary equivalence (missing data). Other aspects of auxiliary equivalence (completion time and comfort completing questionnaires) suggested potentially meaningful differences. Bayesian analyses typically replicated these results, with some notable exceptions regarding auxiliary equivalence. [ABSTRACT FROM AUTHOR]

Abstract: In order to prevent tunnel vision, and ultimately miscarriages of justice, police, prosecutors, and judges must remain open to alternative scenarios in which the suspect is in fact innocent. However, it is not evident from the literature that people are sufficiently aware of how alternative scenarios should be employed in the decision‐making process. In the present research, participants read a case vignette and formed an impression of the suspect's guilt. Some participants were made familiar with an alternative scenario. Others were not only presented with an alternative scenario but were also instructed to score (with pen and paper) the extent to which every piece of evidence fitted in the primary and the alternative scenario. Findings suggest that this pen‐and‐paper task helped to reduce tunnel vision. [ABSTRACT FROM AUTHOR]

With the spread of the Internet and the possibility of online access to articles, a wide range of scientific articles are available to researchers, while finding relevant articles among this substantial number of articles turns out to be a real dilemma. To solve this problem, several scientific paper recommendation algorithms have been proposed. Most of these algorithms suffer from some drawbacks that limit their usability. For example, many of these recommendation methods are designed to recommend papers only to users who had published articles before and can't support new researchers. Also, they usually do not utilize many important features of articles each of which can have a role in determining the relevance of the articles to users. To address these concerns, in this paper, we present the novel method of Integrated Scientific Paper Recommendation with an edge-weight learning approach, called ISPREC++, as an extended version of ISPREC that focuses on learning the weights of edge types in Heterogeneous Information Networks based on users' preferences. ISPREC++ sets the weights of edges in SPIN using a Bayesian Personalized Ranking (BPR) based method and utilizes Gradient Descent to optimize its objective function. Thereafter, it exploits a limited random-walk algorithm for a Top-N recommendation. Extensive experiments on a real-world dataset demonstrate the significant performance superiority of ISPREC++ compared to the state-of-the-art scientific paper recommendation algorithms. [ABSTRACT FROM AUTHOR]

One of the fundamental goals of modern astrophysics is to estimate the physical parameters of galaxies. We present a hierarchical Bayesian model to compute age maps from images in the H α line (taken with Taurus tunable filter, TTF), ultraviolet band (GALEX far UV, FUV), and infrared bands (Spitzer 24, 70, and 160 |$\mu$| m). We present the burst ages for young stellar populations in a sample of nearby and nearly face-on galaxies. The H α to FUV flux ratio is a good relative indicator of the very recent star formation history (SFH). As a nascent star-forming region evolves, the H α line emission declines earlier than the UV continuum, leading to a decrease in the H α/FUV ratio. Using star-forming galaxy models, sampled with a probabilistic formalism, and allowing for a variable fraction of ionizing photons in the clusters, we obtain the corresponding theoretical ratio H α/FUV to compare with our observed flux ratios, and thus to estimate the ages of the observed regions. We take into account the mean uncertainties and the interrelationships between parameters when computing H α/FUV. We propose a Bayesian hierarchical model where a joint probability distribution is defined to determine the parameters (age, metallicity, IMF) from the observed data (the observed flux ratios H α/FUV). The joint distribution of the parameters is described through independent and identically distributed (i.i.d.) random variables generated through MCMC (Markov Chain Monte Carlo) techniques. [ABSTRACT FROM AUTHOR]

There are many proposals in literature for improving the forecast performance. In this paper we applied the regression approach and Bayesian inference to obtain more accurate forecasts of the inflation rate in the case of the Romanian economy. The necessity of using the most accurate forecasts for the inflation rate is required by the realisation of economic criteria for the accession to the eurozone and by the inflation targeting strategy of the National Bank of Romania. Considering the assumption that simple econometric models provide better forecasts than complex models, in this paper we combined various forecasts from individual models using as prior information the expectations of experts. The empirical findings for Romanian inflation rate forecasts over the horizon of 2016-2018 indicated that a fixed effects model performed better than other simple models (autoregressive moving average model, dynamic model, simple and multiple linear model, VAR, Bayesian VAR, simultaneous equations model). The Bayesian combined forecasts that used experts' predictions as priors, with a shrinkage parameter tending to infinity, improved the accuracy of all predictions using individual models, outperforming also naïve forecasts and zero and equal weights forecasts. However, predictions based on Monte Carlo simulation outperformed all the scenarios in terms ofthe mean error and mean absolute error. [ABSTRACT FROM AUTHOR]

This study aims to explore the structure of the households' social capital of rural Vietnam households with secondary data from 2008 to 2018. This paper applied the fundamental theories (resource and network theories) and the Bayesian network to estimate the interaction of proxies to explore the structure of social capital. Results showed that the triangle structure in household social capital with the core point is organization participation. The connections show the tendency from organization participation, linking to household networks. Alongside that, linking social capital and Organization participation are determinants of social capital indicators (social events, social cost). Therefore, this paper suggests employing proxies such as structured indicators for integrating social capital into the livelihood papers. [ABSTRACT FROM AUTHOR]

The main topics of the presented papers focus on various aspects of maritime operations and security, including anomaly detection in maritime traffic, collision risk assessment, and the use of Automatic Identification System (AIS) data for enhancing maritime safety and surveillance. These papers cover a wide range of subjects within the maritime domain, such as trajectory clustering, kinematic behaviour analysis, Bayesian networks for risk assessment, resilience analysis of shipping networks, and the development of novel methods for detecting abnormal maritime behaviour. The emphasis is on using data-driven approaches, statistical methodologies, and advanced technologies to improve maritime operations and security. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data, resulting in more accurate clustering performance. To address the difficulty of hyperparameter selection in deep subspace clustering, this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering, prior knowledge constraints, and model loss weights. Extensive experiments on standard clustering datasets, including ORL, Coil20, and Coil100, validate the effectiveness of the MAS-DSC algorithm. The results show that with its multi-scale network structure and Bayesian hyperparameter optimization, MAS-DSC achieves excellent clustering results on these datasets. Furthermore, tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework. [ABSTRACT FROM AUTHOR]

The article presents information on changes at the journal that have been implemented or will be implemented soon that will entice the home run paper. It provides recent examples of home run papers in the area of Bayesian statistics research. It describes how the home run paper will involve both practitioners and doctoral students. To motivate authors to strive for home runs, the journal will adopt policies that will involve procedural changes and some will involve content changes, including the use of modern technology to spread the message from the papers.

The main purposes of this paper are to derive Bayesian acceptance sampling plans regarding the number of defects per unit of product, and to illustrate how to apply the methodology to the paper pulp industry. The sampling plans are obtained following an economic criterion: minimize the expected total cost of quality. It has been assumed that the number of defects per unit of product follows a Poisson distribution with process average λ, whose prior information is described either for a gamma or for a non- informative distribution. The expected total cost of quality is composed of three independent components: inspection, acceptance and rejection. Both quadratic and step-loss functions have been used to quantify the cost incurred for the acceptance of a lot containing units with defects. Combining the prior information on λ with the loss functions, four different sampling plans are obtained. When the quadratic-loss function is used, an analytical relation between the optimum settings of the sample size and the acceptance number is derived. The robustness analysis indicates that the sampling plans obtained are robust with respect to the prior distribution of the process average as well as to the misspecification of its mean and variance. [ABSTRACT FROM AUTHOR]

Background: Aleutian mink disease virus (AMDV) is the cause of a chronic immune complex disease, Aleutian disease (AD), which is common in mink-producing countries. In 2005, implementation of an AMDV eradication programme in Finland created a need for an automated high-throughput assay. The aim of this study was to validate an AMDV-VP2 -recombinant antigen ELISA, which we developed earlier, in an automated assay format for the detection of anti-AMDV antibodies in mink blood and to determine the accuracy of this test compared with the reference standard (counter-current immunoelectrophoresis, CIEP). Methods: A blood sampling method based on filter paper 12-strips (blood combs) and a device to introduce these strips to an ELISA plate for elution of the samples were developed. Blood and serum samples were collected from 761 mink from two farms with low (2%) and high (81%) seroprevalences of AMDV infection in 2008. ELISA sensitivity and specificity were estimated with a Bayesian 2-test 2-population model that allowed for conditional dependence between CIEP and ELISA. Agreement between the two tests was assessed with kappa statistic and proportion agreement. Results: The sensitivity and specificity of the automated ELISA system were estimated to be 96.2% and 98.4%, respectively. Agreement between CIEP and ELISA was high, with a kappa value of 0.976 and overall proportion agreement of 98.8%. Conclusions: The automated ELISA system combined with blood comb sampling is an accurate test format for the detection of anti-AMDV antibodies in mink blood and offers several advantages, including improved blood sampling and data handling, fast sample throughput time, and reductions in costs and labour inputs. [ABSTRACT FROM AUTHOR]

The design for large-scale offshore systems like renewable energy systems as well as ship structures represents the key factor for the investigation and application of such devices. The existing guide for design schemes of offshore systems cannot cover novel large-scale design demand for recent offshore systems, as a result of the fast-growing scale of offshore systems but the late update of guides. To this end, this paper proposes a novel risk estimation approach of alternative design schemes for large-scale offshore systems as a basis to support the design scheme determinations. Initially, the risks of design schemes are analyzed by fault tree analysis. Subsequently, Bayesian networks and fuzzy sets are employed to calculate the reliability of alternative designs that comply with, or deviate from, the existing guides. The risk level of alternative designs is assessed to ensure the better performance of alternative design schemes in terms of safety. The Bayesian network approach proposed also accretions the weak links in the alternative designs. The results of this paper contribute to enhancing the survivability of offshore systems, such as renewable energy systems as well as ship structures. [ABSTRACT FROM AUTHOR]

Technological advancements and the expanding scope of offshore dynamic positioning (DP) applications introduce new challenges in terms of safety and risk control. This paper provides an overview of a proposed methodology for reliability and risk analysis in the context of DP operations. Firstly, two typical DP operation contexts are discussed. Subsequently, a comprehensive survey is conducted to assess the reliability analysis methodologies applicable to DP systems, encompassing techniques such as Failure Mode and Effects Analysis (FMEA), fault tree analysis, Markov models, and Bayesian networks, which are employed to evaluate the technical and human reliability of DP Class 2 and Class 3 systems. Finally, the paper concludes with a discussion of future research directions and challenges pertaining to reliability analysis and risk management in DP operations. [ABSTRACT FROM AUTHOR]

Relay protection rejection and misoperation exist in the existing distribution network, which will affect the fault diagnosis results. To diagnose faults in distribution networks, this paper presents a fault diagnosis method for the distribution network based on the D-S evidence theory Bayesian network. First, the collected relay protection information is divided into two categories, protection information and circuit breaker information; the corresponding Bayesian network model is established based on their respective action logic, and the corresponding component failure probability is obtained by Bayesian backward inference. Second, the fault probabilities obtained from the two Bayesian networks are fused by the D-S evidence theory, and the obtained fault probabilities are used to diagnose the faulty component. Then, using the Bayesian network corresponding to the faulty component to perform Bayesian forward inference, the protection devices and circuit breakers are identified for misoperation or rejection to achieve the fault diagnosis of the distribution network. Finally, the correctness and reliability of the proposed diagnosis method are verified through the analysis of arithmetic cases. [ABSTRACT FROM AUTHOR]

P values linked to null hypothesis significance testing (NHST) is the most widely (mis)used method of statistical inference. Empirical data suggest that across the biomedical literature (1990–2015), when abstracts use P values 96% of them have P values of 0.05 or less. The same percentage (96%) applies for full-text articles. Among 100 articles in PubMed, 55 report P values, while only 4 present confidence intervals for all the reported effect sizes, none use Bayesian methods and none use false-discovery rate. Over 25 years (1990–2015), use of P values in abstracts has doubled for all PubMed, and tripled for meta-analyses, while for some types of designs such as randomized trials the majority of abstracts report P values. There is major selective reporting for P values. Abstracts tend to highlight most favorable P values and inferences use even further spin to reach exaggerated, unreliable conclusions. The availability of large-scale data on P values from many papers has allowed the development and applications of methods that try to detect and model selection biases, for example, p-hacking, that cause patterns of excess significance. Inferences need to be cautious as they depend on the assumptions made by these models and can be affected by the presence of other biases (e.g., confounding in observational studies). While much of the unreliability of past and present research is driven by small, underpowered studies, NHST with P values may be also particularly problematic in the era of overpowered big data. NHST and P values are optimal only in a minority of current research. Using a more stringent threshold, as in the recently proposed shift from P < 0.05 to P < 0.005, is a temporizing measure to contain the flood and death-by-significance. NHST and P values may be replaced in many fields by other, more fit-for-purpose, inferential methods. However, curtailing selection biases requires additional measures, beyond changes in inferential methods, and in particular reproducible research practices. [ABSTRACT FROM AUTHOR]

Much debate exists between Frequentist and Bayesian methods in statistics. In the evaluation of evidence, the likelihood ratio is credited with quantifying the value of evidence in favour of one or other proposition by considering the probability of the evidence conditional on each proposition and this then converts the Bayesian prior odds into the posterior odds. Motivated by this approach, this paper considers an alternative p-value-based likelihood ratio by explicitly taking into account the behaviour of the Frequentist p-value under both hypotheses, rather than restricting focus solely on the null hypothesis. It is shown that by accommodating the alternative hypothesis, analysis leads to inferential conclusions which are consistent with Bayesian methods. [ABSTRACT FROM AUTHOR]

Based upon failure data (date and odometer reading) in the early operating time, a public transportation company is interested in checking the actual reliability of the door opening system of its subway trains before their warranty expires. We consider non-homogeneous Poisson processes with a double scale because both time and kilometres are recorded for each failure. Different choices to model the relation between operated time and kilometres run are possible: in this paper the kilometres run are incorporated within the intensity function as a random function of time, modelled as a gamma process. Furthermore a periodic component is introduced to deal with the seasonality in the data. Bayesian inference is then carried out via Monte Carlo simulation, obtaining prediction intervals for the expected number of failures during periods of desired length, using only part of the data. The predictions are then compared with the observed data. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Aim: Bayesian statistical methods can allow for more complete and accurate incorporation of evidence in meta‐analyses. However, these methods remain under‐utilized. Methods: A scoping review was conducted to examine the proportion of biomedical meta‐analyses that used Bayesian methods in the period 2005–2016. The review also examined the reproducibility of the work, the cited sources, the reasons for it, its success or failure, the type of model and prior distributions, and whether a mixture of Bayesian and frequentist methods were employed. Results: We found that 1% of meta‐analyses are Bayesian and that the reporting and conduct of these were often poor. Data were published in 41% of analyses, and programs to run the analysis in 18%. Network meta‐analysis was the most common reason and became increasingly popular in recent years. In the majority of papers, models and distributions were either not reported or explained in such brief and ambiguous terms as to be uninformative. Conclusions: More use needs to be made of Bayesian meta‐analysis, and reporting needs to be improved. Greater awareness of these methods and access to training in them is essential. [ABSTRACT FROM AUTHOR]

Comments on an analysis of the structure of a scientific paper on plate tectonics using philosophical models. Basis of a survey of scientific papers; Correctness of the experimental result; Use of Bayesianism to account for the argument for the theoretical explanation of the experimental result.

Companies are usually overloaded with data that they may not know how to take advantage of. On the other hand, artificial intelligence (AI) techniques are known to "keep learning" as the data increase. In this context, our research question emerges: what AI-based methods, in the literature, could be used to automatize business processes and support the decision-making processes of companies? To fill this gap, in this paper, we performed a review of the literature to identify these techniques. We ensured the usage of methods since they allowed reproducibility and extensions. We applied our search string in the Scopus and Web of Science databases and discovered 21 relevant papers pertaining to our question. In these papers, we identified methods that automated tasks and helped analysts make assertive decisions when designing, extending, or reengineering business processes. The authors applied diverse AI techniques, such as K-means, Bayesian networks, and swarm intelligence. Our analysis provides statistics about the techniques and problems being tackled and point to possible future directions. [ABSTRACT FROM AUTHOR]

Lithium‐ion batteries (LIBs) have wide‐ranging applications in areas such as electric vehicles and mobile devices. Accurate estimation of the state of health (SOH) of batteries is an important aspect of battery state estimation. Battery capacity cannot be precisely measured due to negative factors such as aging effects. To address this issue, this paper proposes a LIB's SOH estimation method based on incremental energy analysis (IEA) and transformer. First, data collected during the constant‐current (CC) charging phase of the battery are used to create and analyze the IEA curve. Then, the peaks and areas of the curve are proposed as health characteristics of the LIB. Cosine similarity analysis (CSA) is employed to determine the correlation between each health characteristic and SOH, as well as the correlations between different health characteristics. Finally, an accurate estimation model of battery health was developed using the Bayesian‐transformer model by plotting the relationship between health characteristics and battery health. To validate the reliability of the model, comparisons with regression evaluation metrics of other models such as support vector regression (SVR) and recurrent neural network (RNN) are conducted under different charging rates. The conclusion is drawn that this model exhibits an R2 greater than 98%, MAE less than 0.22%, RMSE less than 0.26%, and MAPE less than 0.0026%. Its accuracy is significantly improved compared to the same type of methods, and it can be used for high accuracy estimation of SOH in LIBs. The multistep prediction of a single step adopted by the model can effectively overcome the capacity regeneration problem in the field of SOH estimation, which will inspire future experts and scholars to improve the accuracy of SOH estimation. [ABSTRACT FROM AUTHOR]

The article discusses the issues related to the bayesian analysis and likelihood-ratio tests for interpretation of evidences in legal cases for giving appropriate judgments. It highlights the accuracy and precision of the non-scientific approaches for assessment of evidences. Also investigated is feasibility of the approaches in coming to a conclusion for appropriate judgments in different court cases.

The article discusses the criticism of the court against the evidences presented in R v. T legal case. It mentions that the juries expressed their concern regarding the illogical method implied to prove the crime against the accused. Also investigated are deformities in the bayesian analysis and likelihood-ratio tests approaches for assessment of evidences in legal cases.

Many theoretical methods have been applied to research user behaviour and requirements. However, the uncertainty associated with customer characteristics often biases the conclusions drawn from customer research and affects the effectiveness of product design. In this paper, Bayesian networks (BN) are introduced into the research on customer behaviour analysis based upon theory of planned behaviour (TPB), and an analysis model driven by customer research data is established from the perspective of user behaviour intention to guide design optimisation. Combining the User background Factor with the TPB Factor, the model analyses the uncertainty of the association between the two, and corrects the errors in the designer's prior knowledge through structural learning. By a case study the paper finds that the evaluations that enhance customers' subjective norms and perceived behavioural control lead to a greater probability of purchase or use. In addition, customers with specific characteristics are more inclined to generate behaviour intention. The paper finally provides a design optimisation plan based upon the result of the research and discusses about the advantages of the research approaches and the directions of future researches. [ABSTRACT FROM AUTHOR]

The determination of plotting positions on probability graph paper so that the associated weighted least squares estimators of the scale parameter and the percentiles of a continuous distribution have certain properties is discussed. Necessary and sufficient conditions are given for an invariant optimal plot for percentile estimation. Also discussed is the derivation of ordered plotting positions. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To investigate the overall probability of error in preparing and administering intravenous medicines; to identify at which stage of the process an error is most likely to occur; and to determine the impact of error correction on the error probability. DESIGN: Systematic review and random-effects Bayesian conditional independence modelling. METHODS: Medline and EMBASE were searched for studies on intravenous medicines. The error rates of each stage were extracted. These, expert estimates, and error rates from generic tasks, were used in a Bayesian conditional independence model to find error ‘hot-spots.’ The main outcome measure was the probability of at least one error occurring during intravenous therapy. RESULTS: Nine published studies were identified for inclusion in the systematic review and meta-analysis. The overall probability of making at least one error in intravenous therapy was 0.73 (95% credible interval (CrI) 0.54 to 0.90). If error-checking was introduced at each stage of the process, the overall rate fell to 0.22 (95% CrI 0.14 to 0.31). Errors were most likely in the reconstitution step. Removing the reconstitution step by providing preprepared injections would reduce the overall error rate to 0.17 (95% CrI 0.09 to 0.27). CONCLUSIONS: Intravenous therapy is complex and error-prone. Error-checking at each stage could reduce the error probability. The use of preprepared injections may help by eliminating errors in the reconstitution of drug and diluent. However, it will be important to ensure that benefits are not outweighed by practical disadvantages such as an increase in selection errors. [ABSTRACT FROM AUTHOR]

The article presents discussion of a paper by statistician I.J. Good. The author supposes that most statisticians approaching the problem of testing goodness of fit would have been very much less ambitious than Good has been. Most of the people, if spend some time looking at the first planet, Mercury and the last two, Neptune and Pluto, do not agree with Bode's law, whereas all the intermediate planets from Venus to Uranus seem to fit the law remarkably well. Those statisticians who were sufficiently knowledgeable would presumably meditate on the matters that Good has meditated on; but, having done this, most people would make a merely informal judgment and completely shirk the challenge that Good has met, to evaluate the weight of evidence. Good remarks that if one is going to test goodness of fit of a theory to some data, using either a Neyman-Pearson approach or a Bayesian approach, one have to frame explicit hypotheses. Over a good many years the author has, from time to time tried to think about the general problem of testing goodness of fit, and remain skeptical about whether goodness of fit can be adequately discussed either in Neyrnan-Pearson or in Bayesian terms because of this necessity to have, not only an explicit null hypothesis, but also a fully defined explicit alternative hypothesis or set of alternative hypotheses.

Members of Amphidinium Claparède and Lachmann constitute a major part of sand-dwelling benthic dinoflagellates worldwide. The genus is traditionally defined by its small epicone size, not exceeding one third of the total cell length. It has long been suspected that this functional definition does not reflect phylogeny, yet the problem of identifying the type species A. operculatum and closely related species has until now hindered attempts to redefine the genus. In this study 12 Amphidinium species were examined using phylogenetic analyses based on nuclear-encoded, partial, large subunit (LSU) rDNA, with a further six Amphidinium species being included in a morphological cladistic analysis. The species selected represented taxa with a range of morphological dissimilar epicone forms. Both cladistic analysis and analyses based on partial LSU rDNA revealed that Amphidinium species with minute left-deflected epicones formed a monophyletic clade that included the type species. Amphidinium species with other epicone types were found to be unrelated to this clade. The type species A. operculatum was identified based on general cell shape and size, position of a dark organelle previously defined as a stigma, and origin of the sulcus. The description of A. elegans by Grell and Wohlfarth-Bottermann was found to be identical to it. A species fitting the original description of A. operculatum was cultured and included in the analyses. Based on cladistic and molecular analyses, it grouped together with all other species with minute left-deflected epicones, and this group constitutes the true genus Amphidinium sensu stricto. An emendation of the genus definition is presented. [ABSTRACT FROM AUTHOR]

In conservation biology, uncertainty about the choice of a statistical model is rarely considered. Model-selection uncertainty occurs whenever one model is chosen over plausible alternative models to represent understanding about a process and to make predictions about future observations. The standard approach to representing prediction uncertainty involves the calculation of prediction (or confidence) intervals that incorporate uncertainty about parameter estimates contingent on the choice of a “best” model chosen to represent truth. However, this approach to prediction based on statistical models tends to ignore model-selection uncertainty, resulting in overconfident predictions. Bayesian model averaging (BMA) has been promoted in a range of disciplines as a simple means of incorporating model-selection uncertainty into statistical inference and prediction. Bayesian model averaging also provides a formal framework for incorporating prior knowledge about the process being modeled. We provide an example of the application of BMA in modeling and predicting the spatial distribution of an arboreal marsupial in the Eden region of southeastern Australia. Other approaches to estimating prediction uncertainty are discussed. [ABSTRACT FROM AUTHOR]

Gives suggestions for the presentation of research results from frequentist, information-theoretic and Bayesian analysis paradigms. How the methods offer alternative approaches to data analysis and inference compared to traditionally used methods; Guidelines on presentation of results using the alternative procedures; Recommendations on the reporting of the results of statistical tests of null hypotheses.

This is an invited discussion of review paper “Nonparametric Bayesian Inference in Applications” by Peter Müller, Fernando A. Quintana and Garritt L. Page. [ABSTRACT FROM AUTHOR]

Traditionally, the ampacity of an overhead transmission line (OHTL) is a static value obtained based on adverse weather conditions, which constrains the transmission capacity. With the continuous growth of power system load, it is increasingly necessary to dynamically adjust the ampacity based on weather conditions. To this end, this paper models the heat balance relationship of the OHTL based on a BP neural network using Bayesian optimization (BO-BP). On this basis, an OHTL ampacity prediction method considering the model error is proposed. First, a two-stage current-stepping ampacity prediction model is established to obtain the initial ampacity prediction results. Then, the risk control strategy of ampacity prediction considering the model error is proposed to correct the ampacity based on the quartile of the model error to reduce the risk of the conductor overheating caused by the model error. Finally, a simulation is carried out based on the operation data of a 220-kV transmission line. The simulation results show that the accuracy of the BO-BP model is improved by more than 20% compared with the traditional heat balance equation. The proposed ampacity prediction method can improve the transmission capacity by more than 150% compared with the original static ampacity. [ABSTRACT FROM AUTHOR]

Neural network models, such as BP, LSTM, etc., support only numerical inputs, so data preprocessing needs to be carried out on the categorical variables to convert them into numerical data. For unordered multi-categorical variables, existing encoding methods may produce dimensional catastrophes and may also introduce additional order misrepresentation and distance bias in neural network computation. To solve the above problems, this paper proposes an unordered multi-categorical variable encoding method O-AE using orthogonal matrix for encoding and encoding representation learning and dimensionality reduction via an autoencoder. Bayesian optimization is used for hyperparameter optimization of the autoencoder. Finally, seven experiments were designed with the basic O-AE, Bayesian optimization of the hyperparameters of the autoencoder for O-AE, and other encoding methods to encode unordered multi-categorical variables in five datasets, and they were input into a BP neural network to carry out target prediction experiments. The results show that the experiments using O-AE and O-AE-b have better prediction results, proving that the method proposed in this paper is highly feasible and applicable and can be an optional method for the data processing of unordered multi-categorical variables. [ABSTRACT FROM AUTHOR]

This paper presents a multiple step-stress accelerated life test using type II censoring. Assuming that the lifetimes of the test item follow the gamma distribution, the maximum likelihood estimation and Bayesian approaches are used to estimate the distribution parameters. In the Bayesian approach, new parametrizations can lead to new prior distributions and can be a useful technique to improve the efficiency and effectiveness of Bayesian modeling, particularly when dealing with complex or high-dimensional models. Therefore, in this paper, we present two sets of prior distributions for the parameters of the accelerated test where one of them is based on the reparametrization of the other. The performance of the proposed prior distributions and maximum likelihood approach are investigated and compared by examining the summaries and frequentist coverage probabilities of intervals. We introduce the Markov Chain Monte Carlo (MCMC) algorithms to generate samples from the posterior distributions in order to evaluate the estimators and intervals. Numerical simulations are conducted to examine the approach's performance and one-sample lifetime data are presented to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Many U.S. states require election workers to check signatures on ballots returned by mail, causing tens of thousands of ballots to be rejected at each general election. This paper applies Bayes' theorem, along with findings from prior research, to show how rejection rates are likely to vary under plausible assumptions about signature checker accuracy and the prevalence of invalid signatures. This approach yields a pair of predictions. On the one hand, given findings from research on handwriting forensics, the Bayesian analysis implies election workers are more likely to wrongly reject valid ballots for purported signature mismatch than to correctly reject invalidly signed returns. On the other hand, research on election workers as problem-solvers suggests they may try to minimize the wrongful rejection of ballots. I find empirical support for each prediction, including signs that election workers learn from past errors to calibrate signature rejection rates downwards. This suggests election worker discretion may be an unexpected source of resilience against laws otherwise liable to disenfranchise eligible voters, although I also discuss conditions under which such discretion may conflict with democratic norms. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of mechanism design optimization, particularly focusing on the energy efficiency and design space of reciprocating mechanisms. The research question centers on how to effectively utilize Computer-Aided Design (CAD) simulations alongside Bayesian optimization (BO) and a constrained design space to streamline the design optimization process, overcoming the limitations of traditional kinematic and dynamic analysis methods. The objective is to investigate and develop a novel optimization framework that integrates CAD-based simulations with a BO approach. To achieve this, the study employs a methodological approach. At first, the feasibility of a chosen mechanism design is evaluated through a sequence of CAD-motion simulations to quantify the (in)feasibility of this design. When this design appears to be feasible, a CAD-based design evaluation method is started, in which the objective value is extracted by a sequence of CAD-motion simulations. In this paper, we advocate the use of non-parametric Gaussian processes to build a surrogate model of the objective function and the feasible design space constrained by static and dynamic constraints. The main research results demonstrate that the proposed CAD-based Bayesian optimization framework can effectively identify optimal design parameters that minimize the root mean square (RMS) torque while adhering to specified static and dynamic constraints. This optimization approach significantly reduces the complexity associated with analytic methods, making it scalable to more complex mechanisms and implementable by machine builders. In conclusion, the study successfully develops a novel optimization framework that leverages CAD-based simulations and Bayesian optimization to streamline the design process of mechanisms. The results of an emergency ventilator case study with three design parameters show a reduction of the RMS torque with 71% after 255 CAD-based design evaluations. Moreover, the results demonstrate the effectiveness of incorporating constraints into the design optimization process and the usage of Bayesian optimization (BO), providing insights into the obtained optimum. This approach offers probabilistic insights into the expected improvement of the optimum, highlighting that the full optimization potential is utilized in a computationally efficient manner. [ABSTRACT FROM AUTHOR]

A System-of-Systems (SoS) is characterized both by independence and by inter-dependency. This inter-dependency, while allowing an SoS to achieve its objectives, also means that failures can cascade throughout the SoS. An SoS needs to be resilient to deal with the impact of complex internal and external environments. In this paper, we propose a resilience analysis method of an SoS based on a hierarchy structure. Firstly, we establish a hierarchy structure, which is ranked from high to low as capability level, activity level and system level. Then, Bayesian Networks (BNs) and Dynamic Bayesian Networks (DBNs) are used to analyze the resilience of the SoS. A resilience-based system importance metric is introduced, which is used in the budget allocation optimization problem during the development domain of an SoS. This paper proposes a mathematical programming model aimed at optimizing SoS resilience by optimally using budget to the subsystem. The application of the proposed approach is demonstrated using a case study: a Next Generation Air Transportation setting. The study results provide evidence that the proposed inter-dependency analysis based on Bayesian theory and the SoS resilience design approach can assist SoS system engineers in increasing expected SoS resilience during the development domain. [ABSTRACT FROM AUTHOR]

As the lifeblood of national energy, oil and gas pipelines are vulnerable to corrosion, third-party damage, and natural disasters. Among these, third-party damage accidents have a high probability and serious consequences. However, China lacks a mature and unified pipeline accident database, making it difficult to assess and manage the risk of such incidents. To address this issue, this paper identifies 20 key risk factors for third-party damage to oil and gas pipelines through literature and case studies. The paper then proposes a multistate probabilistic assessment model using the Decision-Making Trial and Evaluation Laboratory (DEMATEL), interpretative structural modeling method (ISM), Røed method, and Bayesian network (BN). Real case studies demonstrate that the model not only predicts the probability of third-party damage risks but also clearly identifies key influencing factors and causal chains in the risk system. This provides a new approach for assessing third-party damage risks to oil and gas pipelines and offers a scientific basis for decision-making in managing actual pipelines. Practical Applications: The high probability and serious consequences of third-party damage accidents of oil and gas pipelines have caused certain hidden dangers to social security, environment, and economy, and put forward higher requirements for pipeline operation and management. In order to scientifically manage and effectively control the occurrence of third-party damage accidents of oil and gas pipelines, this paper, in the absence of an applicable pipeline failure database. Based on expert knowledge, a model that can be used for the assessment and prediction of third-party damage probability of oil and gas pipelines is constructed. Through the application of real cases, it is shown that the model can be used not only to predict the probability of third-party damage of oil and gas pipelines, but also to identify the key influencing factors and key causal chains leading to third-party damage. The results of the study not only provide theoretical support for pipeline management in reality, but also provide new ideas for risk assessment of oil and gas pipelines that lack actual data. [ABSTRACT FROM AUTHOR]

The editors of I Integrated Environmental Assessment and Management i and the Society of Environmental Toxicology and Chemistry (SETAC) are pleased to announce the selection of best papers published in 2021. I Integrated Environmental Assessment and Management i , 17(6), 1151-1161. https://doi.org/10.1002/ieam.4406 Jonay Neris, Swansea University, Swansea, UK and Universidad de La Laguna, La Laguna, Spain Cristina Santin, Swansea University, Swansea, UK and UMIB-CSIC, Mieres, Spain Roger Lew, University of Idaho, Moscow, Idaho, USA Peter R. Robichaud, Rocky Mountain Research Station, US Forest Service, Moscow, Idaho, USA William J. Elliot, Rocky Mountain Research Station, US Forest Service, Moscow, Idaho, USA Sarah A. Lewis, Rocky Mountain Research Station, US Forest Service, Moscow, Idaho, USA Gary Sheridan, University of Melbourne, Melbourne, Australia Ann-Marie Rohlfs, WaterNSW, New South Wales, Australia Quinn Ollivier, WaterNSW, New South Wales, Australia Lorena Oliveira, WaterNSW, New South Wales, Australia Stefan H. Doerr, Swansea University, Swansea, UK Extreme rainfall following the disastrous 2019/2020 Australian bushfires threatened Sydney's water supply, giving researchers a unique opportunity to refine end-user tools for reliably simulating run-off erosion events in large fire-affected areas. The study by Neris et al. demonstrates a highly successful, international, and cross-sectoral collaboration between local Australian water managers and researchers in Australia, the United Kingdom, the United States, and Europe. [Extracted from the article]

It is common in financial markets for market makers to offer prices on derivative instruments even though they are uncertain about the underlying asset's value. This paper studies the mathematical problem that arises as a result. Derivatives are priced in the risk-neutral framework, so as the market maker acquires more information about the underlying asset, the change of measure for transition to the risk-neutral framework (the pricing kernel) evolves. This evolution takes a precise form when the market maker is Bayesian. It is shown that Bayesian updates can be characterized as additional informational drift in the underlying asset's stochastic process. With Bayesian updates, the change of measure needed for pricing derivatives is two-fold: the first change is from the prior probability measure to the posterior probability measure, and the second change is from the posterior probability measure to the risk-neutral measure. The relation between the regular pricing kernel and the pricing kernel under this two-fold change of measure is characterized. [ABSTRACT FROM AUTHOR]