David B. Dunson
- Arts and Sciences Professor of Statistical Science
- Professor of Statistical Science
- Professor in the Department of Electrical and Computer Engineering (Secondary)
- Faculty Network Member of the Duke Institute for Brain Sciences
Development of Bayesian statistical methods and approaches for uncertainty quantification motivated by applications with complex and high-dimensional data. A particular interest is in high-dimensional low sample size data in which it is necessary to incorporate dimensional reduction through carefully designed prior distributions and challenges arise in efficiently computing posterior approximations. Ongoing focus areas include new algorithms for approximating posterior distributions in big data settings, nonparametric Bayes probability modeling allowing for uncertainty in distributional assumptions, analysis of network data, incorporating physical and geometric prior knowledge in modeling and novel models for dimension reduction for "object data" (functions, tensors, shapes, etc). Primary application areas include genomics, neurosciences, epidemiology, and reproductive studies but with much broader interests in developing new methods motivated by difficult applications (in art, music, radar, imaging processing, etc).
Zhu, H, Strawn, N, and Dunson, DB. "Bayesian graphical models for multivariate functional data." Journal of Machine Learning Research 17 (October 1, 2016): 1-27.
Sarkar, A, and Dunson, DB. "Bayesian Nonparametric Modeling of Higher Order Markov Chains." Journal of the American Statistical Association 111.516 (October 2016): 1791-1803. Full Text
Durante, D, Dunson, DB, and Vogelstein, JT. "Nonparametric Bayes Modeling of Populations of Networks." Journal of the American Statistical Association (August 26, 2016): 1-15. Full Text Open Access Copy
Li, D, Heyer, L, Jennings, VH, Smith, CA, and Dunson, DB. "Personalised estimation of a woman's most fertile days." The European journal of contraception & reproductive health care : the official journal of the European Society of Contraception 21.4 (August 2016): 323-328. Full Text
Hultman, R, Mague, SD, Li, Q, Katz, BM, Michel, N, Lin, L, Wang, J, David, LK, Blount, C, Chandy, R, Carlson, D, Ulrich, K, Carin, L, Dunson, D, Kumar, S, Deisseroth, K, Moore, SD, and Dzirasa, K. "Dysregulation of Prefrontal Cortex-Mediated Slow-Evolving Limbic Dynamics Drives Stress-Induced Emotional Pathology." Neuron 91.2 (July 2016): 439-452. Full Text
Kunihama, T, Herring, A, Halpern, C, and Dunson, D. "Nonparametric Bayes modeling with sample survey weights." Statistics & Probability Letters 113 (June 2016): 41-48. Full Text
Rao, V, Lin, L, and Dunson, DB. "Data augmentation for models based on rejection sampling." Biometrika 103.2 (June 2016): 319-335. Full Text Open Access Copy
Guhaniyogi, R, and Dunson, DB. "Compressed Gaussian process for manifold regression." Journal of Machine Learning Research 17 (May 1, 2016).
Ovaskainen, O, Abrego, N, Halme, P, and Dunson, D. "Using latent variable models to identify large networks of species-to-species associations at different spatial scales." Ed. D Warton. Methods in Ecology and Evolution 7.5 (May 2016): 549-555. Full Text
Yang, Y, and Dunson, DB. "Bayesian Conditional Tensor Factorizations for High-Dimensional Classification." Journal of the American Statistical Association 111.514 (April 2, 2016): 656-669. Full Text