Statistics Research

Bayesian Methods

The majority of our faculty place emphasis on Bayesian methods in their research. Bayesian statistical methods start with a preset distributional idea about quantities (the prior distribution). Data is then collected and used to update prior information in a statistically rigorous manner – the resulting updated distribution is called the posterior. Bayesian methods are very flexible and have immense utility in the sciences. The Bayesian specialization has permitted the department to attract quality faculty and quickly develop an international reputation. This focus is partly responsible for our renowned reputation. Course offerings span the entire statistical gamut and are not limited to Bayesian pursuits. Our Bayesian expertise positions us as a leader in the now expanding era of data science.

Faculty: Athanasios Kottas, Ju Hee Lee, Richard Li, Paul Parker, Raquel Prado, Bruno Sanso