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 ...

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 ...

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 ...

Carlin and Louis - Bayes and Empirical Bayes Methods for Data Analysis Gelman, Carlin, Stern and Rubin - Bayesian Data Analysis Bernardo and Smith - Bayesian Theory Gilks, Richardson and Spiegelhalter ...

A probability is a number that takes some value equal to or between zero and one. If the probability of the 'event' of interest is zero, then the event cannot occur. So, for example, the probability ...