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 has emerged as a powerful methodology for making decisions from data in the applied sciences. Bayesian brings a new way of thinking to statistics, in how it deals with probability, ...

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 ...

Peida Zhan, Hong Jiao, Kaiwen Man, Lijun Wang, Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial, Journal of Educational and Behavioral Statistics, Vol. 44, No. 4 (August 2019), pp. 473 ...

There seem to be a lot of computational biology papers with 'Bayesian' in their titles these days. What's distinctive about 'Bayesian' methods?

Bayesian statistics, by contrast, provide conditional probabilities of parameter values — the plausibility of different parameter values — given the data.

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 ...