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 ...

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 ...

Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...

One of the goals of statistics is to make inferences about population parameters from a limited set of observations. Last month, we showed how Bayes' theorem is used to update probability estimates as ...