Bayesian Statistics

Enabled by computational advances such as Markov chain Monte Carlo methods since late 1980s, Bayesian modeling and analysis are increasingly adopted in biomedical, public health and general data science research. Several researchers in the Department of Biostatistics are contributing to this growth, with important innovations in automated image analysis, in the analysis of ordinal and rank data, and in core statistical methodologies like statistical computing and model assessment. For example, in image analysis, new Bayesian models for spatial processes enable researchers to match anatomically similar regions across image datasets. Ordinal and rank data are common in public health, and Bayesian methods allow such data collected from multiple raters to be combined, and permit the study of rater attributes. Current applications of this methodology include the study of a physician's ability to assign images consistently to disease classes, and the extent to which they agree on the thresholds used in class definitions. Methodological work includes new diagnostics to assess the convergence of numerical algorithms and tools to assess the adequacy of Bayesian models in describing population variability.

Faculty: V. Baladandayuthapani, P. Boonstra, T. Braun, M. Elliott, N. Hederson, N, Kaciroti, J. Kang, K. Kidwelll,  R. Little, J. Morrison, B. Mukherjee, A. Sen, J. Taylor, W. Wen, Z. Wu, L. Zhao, X. Zhou