Submitted by acf199 on

Bayesian Statistics

Bayesian methods have become extremely valuable in scientific research over the past three decades as new computer algorithms (Markov chain Monte Carlo, variational inference, and other approximations) and vast technological improvements in computing have allowed hierarchical Bayesian models to be fit to complex data. Bayesian methods are particularly well suited to combining data from multiple sources, while appropriately accounting for uncertainties throughout the analysis.

Penn State Statistics has several faculty who work on developing Bayesian methods for solving challenging problems. Examples of interdisciplinary research applications for which our faculty are developing Bayesian methods include neuroscience (Nicole Lazar), network models for social science and public health (Maggie Niu), astronomy (Hyungsuk Tak), ecology and disease modeling (Ephraim Hanks and Murali Haran), and statistical genetics/genomics (Xiang Zhu and Justin Silverman).

Faculty
Le Bao
Le
Bao
Professor and Associate Department Head
Email:
Phone:
(814) 865-9443
Interests:
Bayesian Models, Statistical Computing, Small Area Estimation, Diagnostics for Complicated Statistical Models, Applications in Public Health and Social Sciences
Duncan Fong
Duncan
Fong
Professor of Marketing and Statistics
Email:
Phone:
(814) 863-3541
Interests:
Marketing Research, Marketing Analytics, Bayesian Analysis, Operations & Supply Chain Management
Maggie Niu
Maggie
Niu
Associate Professor, Director of the Statistical Consulting Center
Email:
Phone:
(814) 863-8127
Interests:
Bayesian Statistics, Latent Variable Models, Multivariate Analysis, Network Models
Justin Silverman
Justin
Silverman
Assistant Professor of Information Science & Technology, Statistics, and Medicine
Email:
Phone:
(814) 863-8304
Interests:
Bayesian Statistics, Partially Identified Models, Model Misspecification, Count Data, Machine Learning, Genomics