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Mueller (Müller), Peter

Peter Mueller (Müller)

Professor, Affiliated Faculty, Oden Institute
Department of Mathematics, Department of Statistics and Data Sciences

Bayesian Statistics, Nonparametric Bayes, Biostatistics, Computational Statistics


Phone: 512-471-6871

Office Location

Postal Address
AUSTIN, TX 78712

Peter Mueller (Müller) joined The University of Texas at Austin faculty in 2011 sharing joint appointments in the Department of Statistics and Data Sciences and the Department of Mathematics. He is a member of the Oden Institute for Engineering and Computational Sciences and adjunct professor in the Department of Information, Risk, and Operations Management, Red McCombs School of Business. He is an adjunct professor of Biostatistics at M.D. Anderson Cancer Center. Before coming to Austin, he served on the faculty in the Institute of Statistics and Decision Science at Duke University and in the Department of Biostatistics at M.D. Anderson Cancer Center. He is an elected fellow of the International Society of Bayesian Analysis (ISBA), the American Statistical Association (ASA), and the Institute of Mathematical Statistics (IMS), and has been awarded the Zellner Medal (ISBA). He has served as elected president of the International Society of Bayesian Analysis (ISBA) and chair of the Section on Bayesian Statistical Science of the ASA (ASA/SBSS). Mueller has authored more than 210 scientific publications, 10 books and edited volumes and has served on the editorial board of three scientific journals and technical series.


Ph.D., Statistics, Purdue University, 1991

My research interests are broadly in nonparametric Bayesian inference (BNP), Bayesian adaptive clinical trial design, Bayesian bioinformatics, optimal design and decision problems and computational methods for Bayesian inference. My research includes extensive work in BNP, in particular priors for random structures, such as partitions, feature allocation and graphs, and the construction of probability models for multiple, dependent probability measures as it is needed for borrowing strength across related studies or subpopulations. I have worked on decision theoretic and model-based methods for biomedical research problems, including adaptive clinical trial design, subgroup analysis, and treatment individualization. In Bayesian bioinformatics I have worked on tumor heterogeneity and other problems.

Refereed Papers

1. Jiang, F., Lee, J.J., and Muller, P. (2013) A Bayesian decision-theoretic sequentialresponse adaptive randomization design," Statistics in Medicine, to appear, DOI:10.1002/sim.5735.

2. Alejandro Cruz-Marcelo, Gary L. Rosner, Peter Muller, Clinton F. Stewart (2013), "E ect on Prediction when Modeling Covariates in Bayesian Nonparametric Models," Journal of Statistical Theory and Practice, to appear.

3. Lee, J., Quintana, F., Muller, P. and Trippa, L. (2013), "De ning Predictive Probability Functions for Species Sampling Models," Statistical Science, to appear.

4. Mitra, R., Muller, P., Liang, S., Yue, L, and Ji, Y. (2013), "A Bayesian Graphical Model for Chip-Seq Data on Histone Modi cations," Journal of the American Statistical Association, to appear.

5. Leon-Novelo, L., Muller, P., Do, K-A., Arap, W., Sun, J. and Pasqualini, R. (2012), "Semi-Parametric Bayesian Inference for Phage Display Data," Biometrics, to appear, DOI: 10.1111/j.1541-0420.2012.01817.x.

6. Rossell, D. and Muller, P. (2013) Sequential sample sizes for high-throughput hypothesis testing experiments," Biostatistics, 14, 75-86.

7. Leon-Novelo, L., Muller, P., Do, K-A., Arap, W., Sun, J. and Pasqualini, R. (2012), "Bayesian Decision Theoretic Multiple Comparison Procedures: An Application to Phage Display Data", Biometrical Journal, to appear, DOI: 10.1002/bimj.201200051.

8. Ji, Y., Mitra, R., Quintana, F., Muller, P., Jara, A., Liu, P., Lu, Y. and Liang, S. (2012), BM-BC: A Bayesian method of base calling for Solexa sequence data" BMC Bioinformatics, 13:S6, doi:10.1186/1471-2105-13-S13-S6

9. Guoshuai Cai, Hua Li, Yue Lu, Xuelin Huang, Juhee Lee, Peter Muller, Yuan Ji and Shoudan Liang (2012), Accuracy of RNA-Seq and its dependence on sequencing depth," BMC Bioinformatics, 13(Suppl 13):S5 doi:10.1186/1471-2105-13-S13-S5.

10. Di Lucca, M.A., Guglielmi, A., Muller, P., and Quintana, F. (2012), Bayesian autoregressive nonparametric models", Bayesian Analysis, 7, 771{796.

11. Telesca, D., Muller, P, Parmigiani, G., and Freedman, R. (2012), Modeling Dependent Gene Expression", Annals of Applied Statistics, 6, 542-560.

  • Fellow of the American Statistical Association
  • President of the International Society for Bayesian Analysis (2010)
  • Robert R. Herring Dinstinguished Professorship in Clinical Research (2007-2011)