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We point out potential drawbacks of some of Leising et al.'s (2022a) proposed ways how personality science can be improved. We argue that it is ill-advised to use only one measure for a concept. Also, we argue that researchers should not refrain from conducting a study when a high level of statistical power is precluded. Then, we go one step further and formulate additional ideas of how to improve research. Specifically, we argue that it is a good thing to use different methods rather than only one when attempting to generalize across these methods. Moreover, we argue for a more theory-driven strategy for specifying factor analytic models, and we emphasize that high-quality research is often interdisciplinary. Finally, we point to a particular risk associated with any formal reward system. [ABSTRACT FROM AUTHOR]

The conditions for Bayesian posterior robustness are examined in recent literature; however, many of the proofs seem long and complicated. In this article, we first summarize some basic lemmas that have been implicitly or explicitly applied. Next, in a simple case, we give a short proof of posterior robustness illustrating their use. As a by-product, a new and practically relevant condition is obtained. [ABSTRACT FROM AUTHOR]

Sean Carroll has recently argued that theories predicting that observations are dominated by Boltzmann Brains should be rejected because they are cognitively unstable: “they cannot simultaneously be true and justifiably believed.” While such Boltzmann Brain theories are indeed cognitively unstable, one does not need to appeal to this argumentation to reject them. Instead, they may be ruled out by conventional Bayesian reasoning, which is sufficient to keep Boltzmann Brains at bay. [ABSTRACT FROM AUTHOR]

The majority of statistical methods to share information in basket trials are based on a Bayesian hierarchical model with a common normal distribution for the logit-transformed response rates. The methods are of varying complexity, yet they all use this basic model. Generally, complexity is an obstacle for the application in clinical trials and that includes the use of the logit-transformation. The transformation complicates the model and impedes a direct interpretation of the hyperparameters. On the other hand, there exist basket trial designs which directly work on the probability scale of the response rate which facilitates the understanding of the model for many stakeholders. In order to reduce unnecessary complexity, we considered using a hierarchical beta-binomial model instead of the transformed models. This article investigates whether this approach is a practicable alternative to the commonly applied sharing tools based on a logit-transformation of the response rates. For this purpose, we performed a systematic comparison of the two models, starting with the distributional assumptions for the response rates, continuing with the Bayesian behavior together with binomial data in an independent setting and ended with a simulation study for the hierarchical model under various data and prior scenarios. All Bayesian comparisons require equal starting points, wherefore we propose a calibration procedure to choose similar priors for the models. The evaluation of the sharing property additionally required an evaluation measure for simulation results, which we derived in this work. The conclusion of the comparison is that the hierarchical beta-binomial model is a feasible alternative basic model to share information in basket trials. [ABSTRACT FROM AUTHOR]

As a type of magnetic sensor known for its high reliability and long lifespan, the reliability issues of Hall current sensors have attracted attention in fields such as electromagnetic compatibility. However, there is still a lack of sufficient failure data for reliability prediction. Therefore, a small-sample reliability prediction method based on the improved Bayes method is proposed. Firstly, the pseudo-failure lifespan data are acquired through the accelerated degradation testing of Hall current sensors subjected to temperature and humidity stressors, and the life is examined by the Weibull distribution; then, the data expanded using the BP neural network model are used as the a priori information, and the parameter estimation of the Weibull distribution is obtained by the Bootstrap method and Gibbs sampling; finally, the Peck accelerated model is implemented to achieve the normal temperature-humidity reliability prediction of Hall current sensors under stress, and the utility of the enhanced Bayes technique is confirmed through the application of the Wiener stochastic process model. [ABSTRACT FROM AUTHOR]

The transition matrix estimators of the Markov chain are not accurate and the transition matrix is considered given. There are many methods that are used to estimate the transition probabilities matrix for different cases, the most famous of which is the Maximum Likelihood Method, In order to find a good estimator for the transition probabilities matrix of the Markov chain, a Bayes method and a Proposed Method was used in this paper, to reach the transition probabilities with the least variance, The Escherichia Coli (E.Coli) gene chain was chosen as an applied aspect of the study due to its importance in medical research and for the purpose of discovering and manufacturing treatments by knowing the final form of its gene chain. After testing the E.Coli gene chain, it was found that is represents a Markov chain, and then both the transition probabilities matrix and the transition probabilities variance were estimated used Proposed Method and Bayes method and Maximum Likelihood Method, and it was found that the Proposed Method for transitional probabilities is better than the Bayes method and Maximum Likelihood Method dependence on the variance.

There is a growing need to evaluate of multiple competing drugs in phase II trials where the number of patients is often limited, and simultaneous assessment of both efficacy and toxicity is crucial. To avoid the waste of research resources, it is indeed more efficient to screen multiple drugs at once in a platform phase II setting. We aim to adapt the Bayesian optimal phase II (BOP2) design to multi-arm trials for both uncontrolled and controlled settings. The binary efficacy and toxicity endpoints are modeled by a Dirichlet distribution as a vector of four outcomes. Posterior marginal distributions at each analysis are used to derive the monitoring threshold that varies during the trial. We control the family-wise Type I error rate for multiple comparison against a common reference value or a shared control. We conduct simulation studies under both uncontrolled and controlled settings to evaluate the operating characteristics of the proposed design. Our simulations demonstrate that the design exhibits better operating characteristics compared to a design using a constant threshold and is less sensitive to changes in accrual rate relative to what was planned. The design had promising operating characteristics and could be used in phase II oncology clinical trials for evaluating multiple drugs at a time. [ABSTRACT FROM AUTHOR]

The sense of agency – the subjective feeling of being in control of our own actions – is one central aspect of the phenomenology of action. Computational models provided important contributions toward unveiling the mechanisms underlying the sense of agency in individual action. In particular, the sense of agency is believed to be related to the match between the actual and predicted consequences of our own actions (comparator model). In the study of joint action, models are even more necessary to understand the mechanisms underlying the development of coordination strategies and how the subjective experiences of control emerge during the interaction. In a joint action, we not only need to predict the consequences of our own actions; we also need to predict the actions and intentions of our partner, and to integrate these predictions to infer their joint consequences. Understanding our partner and developing mutually satisfactory coordination strategies are key components of joint action and in the development of the sense of joint agency. Here we discuss a computational architecture which addresses the sense of agency during intentional, real-time joint action. We first reformulate previous accounts of the sense of agency in probabilistic terms, as the combination of prior beliefs about the action goals and constraints, and the likelihood of the predicted movement outcomes. To look at the sense of joint agency, we extend classical computational motor control concepts - optimal estimation and optimal control. Regarding estimation, we argue that in joint action the players not only need to predict the consequences of their own actions, but also need to predict partner's actions and intentions (a 'partner model') and to integrate these predictions to infer their joint consequences. As regards action selection, we use differential game theory – in which actions develop in continuous space and time - to formulate the problem of establishing a stable form of coordination and as a natural extension of optimal control to joint action. The resulting model posits two concurrent observer-controller loops, accounting for 'joint' and 'self' action control. The two observers quantify the likelihoods of being in control alone or jointly. Combined with prior beliefs, they provide weighing signals which are used to modulate the 'joint' and 'self' motor commands. We argue that these signals can be interpreted as the subjective sense of joint and self agency. We demonstrate the model predictions by simulating a sensorimotor interactive task where two players are mechanically coupled and are instructed to perform planar movements to reach a shared final target by crossing two differently located intermediate targets. In particular, we explore the relation between self and joint agency and the information available to each player about their partner. The proposed model provides a coherent picture of the inter-relation of prediction, control, and the sense of agency in a broader range of joint actions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dalian Ocean University is the property of Journal of Dalian Ocean University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Introduction: Xinjiang Brown cattle constitute the largest breed of cattle in Xinjiang. Therefore, it is crucial to establish a genomic evaluation system, especially for those with low levels of breed improvement. Methods: This study aimed to establish a cross breed joint reference population by analyzing the genetic structure of 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle (Illumina GeneSeek GGP bovine 150 K chip). The Bayes method single-step genome-wide best linear unbiased prediction was used to conduct a genomic evaluation of the joint reference population for the milk traits of Xinjiang Brown cattle. The reference population of Chinese Holstein cattle was randomly divided into groups to construct the joint reference population. By comparing the prediction accuracy, estimation bias, and inflation coefficient of the validation population, the optimal number of joint reference populations was determined. Results and Discussion: The results indicated a distinct genetic structure difference between the two breeds of adult cows, and both breeds should be consideredwhen constructing multi-breed joint reference and validation populations. The reliability range of genome prediction of milk traits in the joint reference population was 0.142-0.465. Initially, it was determined that the inclusion of 600 and 900 Chinese Holstein cattle in the joint reference population positively impacted the genomic prediction of Xinjiang Brown cattle to certain extent. It was feasible to incorporate the Chinese Holstein into Xinjiang Brown cattle population to forma joint reference population for multi-breed genomic evaluation. However, for different Xinjiang Brown cattle populations, a fixed number of Chinese Holstein cattle cannot be directly added duringmulti-breed genomic selection. Pre-evaluation analysis based on the genetic structure, kinship, and other factors of the current population is required to ensure the authenticity and reliability of genomic predictions and improve estimation accuracy. [ABSTRACT FROM AUTHOR]

The existence of transfer effects of video games on cognitive performance are controversially discussed in experimental psychology. Whereas recent meta-analyses suggest the absence of far transfer effects, empirical evidence regarding near transfer effects is more controversial. This conceptual replication investigated the short-term near transfer effect of playing Tetris on mental rotation abilities. The design of the conceptual replication was based on a comprehensive compilation of the methods used by previous literature on this topic and advanced in order to reach a high scientific state-of-the-art standard. We ran a high-powered conceptual replication study with 366 participants randomly assigned to either an experimental group playing Tetris or a control group playing Solitaire. Both groups completed three commonly used mental rotation tests in a pre- and a posttest session. Additionally, the experimental group played Tetris while the control group played Solitaire. Playing time was 10 hours in total within 4 weeks. Based on previous research, we hypothesized that this might generate a short-term transfer effect of Tetris on mental rotation. While participants showed a repeated testing effect for the mental rotation tests in both groups, we found evidence that Tetris does not produce a short-term transfer effect on mental rotation. Both gender and expected outcomes did not influence this effect. Our study suggests that playing Tetris does not improve mental rotation skills. [ABSTRACT FROM AUTHOR]

Performing joint genomic predictions for multiple breeds (MBGP) to expand the reference size is a promising strategy for improving the prediction for limited population sizes or phenotypic records for a single breed. This study proposes an MBGP model—mbBayesAB, which treats the same traits of different breeds as potentially genetically related but different, and divides chromosomes into independent blocks to fit heterogeneous genetic (co)variances. Best practices of random effect (co)variance matrix priors in mbBayesAB were analyzed, and the prediction accuracies of mbBayesAB were compared with within-breed (WBGP) and other commonly used MBGP models. The results showed that assigning an inverse Wishart prior to the random effect and obtaining information on the scale of the inverse Wishart prior from the phenotype enabled mbBayesAB to achieve the highest accuracy. When combining two cattle breeds (Limousin and Angus) in reference, mbBayesAB achieved higher accuracy than the WBGP model for two weight traits. For the marbling score trait in pigs, MBGP of the Yorkshire and Landrace breeds led to a 6.27% increase in accuracy for Yorkshire validation using mbBayesAB compared to that using the WBGP model. Therefore, considering heterogeneous genetic (co)variance in MBGP is advantageous. However, determining appropriate priors for (co)variance and hyperparameters is crucial for MBGP. [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]

Age estimation is an important component of decedent identification. When assessing adult remains, anthropologists frequently use gross examination of skeletal elements, such as clavicles, ribs, and pubic symphyses. For fleshed bodies, this requires the removal of these elements and maceration prior to analysis. A new method was developed using radiographic imaging to estimate age from degenerative changes of the lower thoracic and upper lumbar vertebrae. This technique will complement anthropological age estimation methods in young and middle‐aged adults and may serve as a stand‐alone method for older individuals. Digital radiographs from 240 medical examiner cases were evaluated. The sample included 120 females and 120 males between the ages of 18 and 101 years. A 3‐phased scoring system was used for the target vertebrae. Transition analysis was conducted on binned average scores and a Bayesian approach was used to assign age intervals. At the 90% credible interval, individuals in Bin 1 were under 36 years of age while those in Bin 3 were over 47 years of age. Individuals in Bin 2 showed too much age variation to be informative. No significant differences were found between males and females. These findings will be especially useful in the age estimation of older adults and may eliminate the need for skeletal sampling in medicolegal cases where advanced degenerative changes are radiographically observed in the lower thoracic and/or upper lumbar vertebrae. This method was developed for use on fleshed individuals but may also be applicable to skeletonized remains. [ABSTRACT FROM AUTHOR]

Decades of findings in psychology suggest that human belief is thoroughly irrational. At best, beliefs might be formed by heuristic processes that predictably lead to suboptimal outcomes. At worst, they are slaves to motivated reasoning, which allows people to come to whichever conclusions they prefer. In this article, we suggest that belief updating, narrowly construed, may be a rational process that is uniquely sensitive to evidence and cognitively impenetrable to desires or incentives. Before any updating can occur, however, a series of processes mediate between information in the world and subjectively compelling evidence. We distinguish between updating proper and processes of evidence search, acceptance, hypothesis specification, integration of relevant information, and reasoning. We review research highlighting the computational difficulty inherent to each of these problems and conclude that solutions must be heuristic and fallible. Beyond incidental failures, evidence evaluation processes—unlike updating—are penetrable to motivation and as such, may be biased by people's desires and goals. In light of this distinction, we propose a theoretical framework for integrating research on belief which divides the cognitive processes involved in belief into two distinct levels. At Level 1, updating is suggested to be approximately Bayesian and impenetrable to desires and goals. In contrast, Level 2 processes, which search for and evaluate evidence, are cognitively penetrable. In addition, we emphasize that Level 2 processes are necessarily heuristic and exhibit bounded rationality (Simon, 1956) given the difficulty of the problems they have to solve. Finally, we specify an additional set of relatively invariant characteristics, which influence how Level 2 processes are employed by making different methods of information processing available. Our framework offers a more nuanced understanding of belief, permits a granular localization of irrationality, and may help reconcile extant debates in the literature. [ABSTRACT FROM AUTHOR]

At present,the safety analysis of aircraft air conditioning system is based on the fixed fault state,without considering the dynamic development and change of the fault. At the same time,only qualitative analysis can be carried out. It is necessary to carry out accurate safety analysis on aircraft air conditioning system. Based on the root fault parameters in the FMEA of the air conditioning system,Markov time series is used to solve the lateral fault transfer path,and the joint probability distribution and Bayes algorithm are used to solve the fault level and probability,thus realizing the lateral qualitative and quantitative safety analysis; The Monte Carlo simulation is used to solve the longitudinal upgrading path,fault level and probability of the fault,and the longitudinal qualitative and quantitative security analysis is realized. The results show that the dynamic quantitative and qualitative safety analysis can accurately determine the safety of the aircraft air conditioning system,the release conclusion and the release requirements under fault conditions,and has good engineering application value.

As a type of magnetic sensor known for its high reliability and long lifespan, the reliability issues of Hall current sensors have attracted attention in fields such as electromagnetic compatibility. However, there is still a lack of sufficient failure data for reliability prediction. Therefore, a small-sample reliability prediction method based on the improved Bayes method is proposed. Firstly, the pseudo-failure lifespan data are acquired through the accelerated degradation testing of Hall current sensors subjected to temperature and humidity stressors, and the life is examined by the Weibull distribution; then, the data expanded using the BP neural network model are used as the a priori information, and the parameter estimation of the Weibull distribution is obtained by the Bootstrap method and Gibbs sampling; finally, the Peck accelerated model is implemented to achieve the normal temperature-humidity reliability prediction of Hall current sensors under stress, and the utility of the enhanced Bayes technique is confirmed through the application of the Wiener stochastic process model.

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]

Bayesian decision theory suggests that optimal decision-making should use and weigh prior beliefs with current information, according to their relative uncertainties. However, some characteristics of borderline personality disorder (BPD) patients, such as fast, drastic changes in the overall perception of themselves and others, suggest they may be underrelying on priors. Here, we investigated if BPD patients have a general deficit in relying on or combining prior with current information. We analyzed this by having BPD patients (n = 23) and healthy controls (n = 18) perform a coin-catching sensorimotor task with varying levels of prior and current information uncertainty. Our results indicate that BPD patients learned and used prior information and combined it with current information in a qualitatively Bayesian-like way. Our results show that, at least in a lower-level, nonsocial sensorimotor task, BPD patients can appropriately use both prior and current information, illustrating that potential deficits using priors may not be widespread or domain-general. [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]

Evidence scholars have observed probabilistic fallacies in legal fact-finding and given them names since the 1980s (for example 'Prosecutor's Fallacy' and 'Defense Attorney's Fallacy'). This has produced a rather un-organised list of over a dozen different probabilistic fallacies. In this article, the author proposes a systematic account where the observed probabilistic fallacies are organised in categories. Hierarchical relations between probabilistic fallacies are highlighted, and some fallacies are re-named to reflect the category they belong to and their relation to other fallacies in that category. All fallacies are precisely defined and illustrated with examples from real cases where they are committed by fact-finders. The result is a list of 12 probabilistic fallacies organised into 7 categories. [ABSTRACT FROM AUTHOR]

Dinas Pekerjaan Umum dan Penataan Ruang (PUPR) Kabupaten Balangan is a Regional Government Organization Unit (SOPD) that has the task of assisting the Bupati in administering government affairs in public works, infrastructure, and housing development. In addition, it also formulates, determines, and implements policies in the field of water resources management, road management, housing provision and development of residential areas, infrastructure financing, structuring of buildings, drinking water supply systems, wastewater management systems, and environmental drainage and waste, and construction services construction. Difficulty in determining priorities for infrastructure development on the Dinas Pekerjaan Umum dan Penataan Ruang Kabupaten Balangan, then an Infrastructure Development Priority Analysis system was created to support the construction of roads and bridges on Kabupaten Balangan. To determine the priority development weights using the AHP method and the order of priorities using the Bayes method because it is one of the techniques used to analyze the best decision-making from several alternatives to produce optimal gains. The results of the completed questionnaire get an average yield of 21 (twenty-one) or 87.5% (eighty-seven point five percent). If the average is included in the rating scale, get a VS rating or Very Satisfied. Decision support systems using AHP and Bayes methods can determine the priority of road infrastructure development at Dinas Pekerjaan Umum dan Penataan Ruang Kabupaten Balangan.

Accounting the problem of small sample size in tomato yield statistics, a dual-fusion prediction analysis model based on Bayes theory and deep learning algorithm Cascade-PSPNET is proposed. The tomato yield prediction is conducted based on remote sensing image time series analysis and multi-source information fusion theory with Bayesian theory and credibility weighting. First, the area of tomato planting is analyzed through semantic segmentation algorithms of remote sensing images during the tomato planting process. The yield is predicted by variables such as planting area fluctuation, disaster cycle fluctuation, etc. Through analyzing the reduced yield affected by disasters in different time periods of remote sensing images during planting process, the value chain of tomato industry is calculated by comprehensively analyzing price-value system, inflation coefficient, and unit area yield. At the same time, the annual patent application volume is used to predict the change of tomato yield year by year according to Bayesian theory, and the relationship between annual patent application volume and tomato yield year by year under different confidence levels is analyzed. The results show that it is feasible to use the Bayes method with semantic segmentation algorithms of remote sensing images to predict tomato yield. Next, the experiment of fusion prediction between two prediction models in the same target area will be carried out for verification. [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]

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]

This article examines the benefits of incorporating religious reflection into the psychology of religion and vice versa. By applying Bayes' theorem, we discover that scientists and theologians can collaborate without sharing prior beliefs. Instead, rationality requires updating our beliefs before data collection in response to the degree of surprise generated by the data. Moreover, although people who start with different beliefs may become more aligned after data collection, rationality does not entail a convergence to identical beliefs. To illustrate the potential for growth in understanding from greater collaboration between theologians and scientists, I examine a longitudinal investigation of religion after a natural disaster. This case study illustrates how conversations between theological and psychological perspectives on religion can lead to a more comprehensive understanding of virtue cultivation, benefiting both science and theology. [ABSTRACT FROM AUTHOR]

In meta‐analyses of rare events, it can be challenging to obtain a reliable estimate of the pooled effect, in particular when the meta‐analysis is based on a small number of studies. Recent simulation studies have shown that the beta‐binomial model is a promising candidate in this situation, but have thus far only investigated its performance in a frequentist framework. In this study, we aim to make the beta‐binomial model for meta‐analysis of rare events amenable to Bayesian inference by proposing prior distributions for the effect parameter and investigating the models' robustness to different specifications of priors for the scale parameter. To evaluate the performance of Bayesian beta‐binomial models with different priors, we conducted a simulation study with two different data generating models in which we varied the size of the pooled effect, the degree of heterogeneity, the baseline probability, and the sample size. Our results show that while some caution must be exercised when using the Bayesian beta‐binomial in meta‐analyses with extremely sparse data, the use of a weakly informative prior for the effect parameter is beneficial in terms of mean bias, mean squared error, and coverage. For the scale parameter, half‐normal and exponential distributions are identified as candidate priors in meta‐analysis of rare events using the Bayesian beta‐binomial model. [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]

Human probability judgments are both variable and subject to systematic biases. Most probability judgment models treat variability and bias separately: a deterministic model explains the origin of bias, to which a noise process is added to generate variability. But these accounts do not explain the characteristic inverse U-shaped signature linking mean and variance in probability judgments. By contrast, models based on sampling generate the mean and variance of judgments in a unified way: the variability in the response is an inevitable consequence of basing probability judgments on a small sample of remembered or simulated instances of events. We consider two recent sampling models, in which biases are explained either by the sample accumulation being further corrupted by retrieval noise (the Probability Theory + Noise account) or as a Bayesian adjustment to the uncertainty implicit in small samples (the Bayesian sampler). While the mean predictions of these accounts closely mimic one another, they differ regarding the predicted relationship between mean and variance. We show that these models can be distinguished by a novel linear regression method that analyses this crucial mean-variance signature. First, the efficacy of the method is established using model recovery, demonstrating that it more accurately recovers parameters than complex approaches. Second, the method is applied to the mean and variance of both existing and new probability judgment data, confirming that judgments are based on a small number of samples that are adjusted by a prior, as predicted by the Bayesian sampler. [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]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Performing joint genomic predictions for multiple breeds (MBGP) to expand the reference size is a promising strategy for improving the prediction for limited population sizes or phenotypic records for a single breed. This study proposes an MBGP model—mbBayesAB, which treats the same traits of different breeds as potentially genetically related but different, and divides chromosomes into independent blocks to fit heterogeneous genetic (co)variances. Best practices of random effect (co)variance matrix priors in mbBayesAB were analyzed, and the prediction accuracies of mbBayesAB were compared with within-breed (WBGP) and other commonly used MBGP models. The results showed that assigning an inverse Wishart prior to the random effect and obtaining information on the scale of the inverse Wishart prior from the phenotype enabled mbBayesAB to achieve the highest accuracy. When combining two cattle breeds (Limousin and Angus) in reference, mbBayesAB achieved higher accuracy than the WBGP model for two weight traits. For the marbling score trait in pigs, MBGP of the Yorkshire and Landrace breeds led to a 6.27% increase in accuracy for Yorkshire validation using mbBayesAB compared to that using the WBGP model. Therefore, considering heterogeneous genetic (co)variance in MBGP is advantageous. However, determining appropriate priors for (co)variance and hyperparameters is crucial for MBGP.

Phương pháp Bayes được sử dụng trong nghiên cứu để xem xét ảnh hưởng của giá dầu đối với khả năng sinh lời của các ngân hàng thương mại, được đại diện bởi tỷ suất sinh lời trên tổng tài sản hay ROA (Return On Asset). Dữ liệu nghiên cứu được thu thập tại 28 ngân hàng thương mại trong giai đoạn 2011-2021. Kết quả nghiên cứu xác suất hậu nghiệm hoàn toàn ủng hộ tác động ngược chiều của giá dầu đối với ROA. Tỷ lệ thu nhập lãi ròng và tỷ lệ thu nhập ngoài lãi có ảnh hưởng cùng chiều đối với ROA; trong khi đó, hệ số an toàn vốn, tỷ lệ chi phí trên thu nhập và tỷ lệ thanh khoản có tác động ngược chiều. Ảnh hưởng của quy mô đến ROA chưa thể xác nhận trong bài viết. Ngoài ra, kết quả nghiên cứu đã cung cấp những bằng chứng thống kê có ý nghĩa về tác động của giá dầu cũng như các yếu tố nội tại đến khả năng sinh lời trong hoạt động ngân hàng.

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.

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]

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]

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]