Asteroseismic observations are crucial to constrain stellar models with precision. Bayesian Estimation of STellar Parameters (BESTP) is a tool that utilizes statistics and nested sampling Monte Carlo algorithm search for the best match given set classical asteroseismic constraints from observations. The computation evaluation efficiently performed in an automated multi-threaded way. To illustra...

In the reliability modeling field, we sometimes encounter systems with uncertain structures, and the use of fault trees and reliability diagrams is not possible. To overcome this problem, Bayesian approaches offer a considerable efficiency in this context. This paper introduces recent contributions in the field of reliability modeling with the Bayesian network approach. Bayesian reliability mod...

Hierarchical Bayesian modeling provides a flexible and interpretable way of extending simple models of cognitive processes. To introduce this special issue, we discuss four of the most important potential hierarchical Bayesian contributions. The first involves the development of more complete theories, including accounting for variation coming from sources like individual differences in cogniti...

Bayesian model averaging enables one to combine the disparate predictions of a number of models in a coherent fashion, leading to superior predictive performance. The improvement in performance arises from averaging models that make different predictions. In this work, we tap into perhaps the biggest driver of different predictions—different analysts—in order to gain the full benefits of model ...

There is a need for powerful and user-friendly tools that analyze the interaction between genes in the bioinformatics area. Bayesian networks are a good choice for this modeling because of the stochastic nature of gene up and down regulation. The Microarray Meta-Analysis Analysis Tool (MMAT) is designed to use sequenced transcriptome data of microarray experiments to produce gene co-expression ...