[ Français ]

Schedule and abstract available here.



Peter Mueller works on Bayesian inference, with a focus on nonparametric Bayesian methods, simulation based methods, optimal design and multiple comparison procedures. He is interested in applications in biostatistics and bioinformatics, including in particular Bayesian clinical trial design, hierarchical models, and population PK / PD models.

He completed his Ph.D. at Purdue University (loooong ago), then joined the statistics department at Duke University, moved to the Department of Biostatistics at M.D. Anderson Cancer Center, and recently joined the new statistics program at UT Austin, remaining adjunct faculty member at M.D. Anderson.