Research areas

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

Biostatistics
Faculty: Athanasios Kottas, Ju Hee Lee, Richard Li

Correlated Data
Faculty: Athanasios Kottas, Robert Lund, Paul Parker, Raquel Prado, Bruno Sanso

Design and Sampling
Faculty: Marcela Cordoba

Mathematical Statistics
Faculty: Marcela Cordoba, Athanasios Kottas, Robert Lund, Ju Hee Lee, Richard Li, Paul Parker, Raquel Prado, Bruno Sanso

Probability
Faculty: Marcela Cordoba, Athanasios Kottas, Robert Lund, Bruno Sanso

Regression Methods
Faculty: Marcela Cordoba, Athanasios Kottas, Robert Lund, Ju Hee Lee, Richard Li, Paul Parker, Raquel Prado, Bruno Sanso

Stochastic Processes
Faculty: Robert Lund, Raquel Prado, Bruno Sanso