In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

Bayesian statistics represents a powerful framework for data analysis that centres on Bayes’ theorem, enabling researchers to update existing beliefs with incoming evidence. By combining prior ...

1 Department of Methodology and Statistics, Utrecht University, Utrecht, Netherlands 2 Department of Psychological Sciences, University of California, Merced, Merced, CA, United States When Bayesian ...

In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy ...

Bayesian statistics have made great strides in recent years, developing a class of methods for estimation and inference via stochastic simulation known as Markov Chain Monte Carlo (MCMC) methods. MCMC ...

This tutorial demonstrates how to conduct a stratified Bayesian survival analysis using a discrete-time beta-binomial approach, as described in the paper: Bayesian Stratified Analysis of Treatment ...

Various inter-disciplinary applications of Bayesian statistics dominated the third day of the international conference on Bayesian theory and applications and regional meeting of International Society ...