Bayesian Inference

The Bayesian paradigm is an attempt to utilize all available information in decision making. Prior knowledge coming from experience, expert judgment or previously collected data is used with current data to characterize the current state of knowledge.

Computational Bayesian methods have become increasingly popular in the era of big data sets subject to irregularities (missing values, censored observations, etc.), often requiring fitting high-dimensional statistical models. These methods allow for the use of models of complex physical phenomena that were previously too difficult to estimate. Theoretical properties of such Bayesian methods offer a means of more fully understanding issues that are central to many practical problems.

Faculty

Sujit Ghosh
Subhashis Ghoshal
Ryan Martin
Brian Reich
Alyson Wilson