David B. Dunson, Arts and Sciences Professor of Statistical Science and Mathematics and Faculty Network Member of Duke Institute for Brain Sciences

David B. Dunson

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).

Office Location:  218 Old Chemistry Bldg, Durham, NC 27708
Office Phone:  (919) 684-8025
Email Address: send me a message
Web Page:  http://www.stat.duke.edu/~dunson/

Teaching (Fall 2018):

Office Hours:

Thurs 9-10am

PhDEmory University1997
Ph.D.Emory University1997
B.S.Pennsylvania State University1994

Bayesian Statistics
Complex Hierarchical and Latent Variable Modelling
Nonparametric Statistical Modelling
Model Selection
Statistical Modeling
Research Interests: Nonparametric Bayes, Latent variable methods, Model uncertainty, Applications in epidemiology & genetics, Machine learning

Current projects: Nonparametric Bayes methods for conditional distributions, Semiparametric methods for high-dimensional predictors, New priors for functional data analysis, Borrowing information across disparate data sources, Methods for identifying gene x environmental interactions

Development of Bayesian methods motivated by applications with complex and high-dimensional data. A particular focus is on nonparametric Bayes approaches for conditional distributions and for flexible borrowing of information. I am also interested in methods for accommodating model uncertainty in hierarchical models, and in latent variable methods, including structural equation models. A recent interest has been in functional data analysis.

Areas of Interest:

Functional data analysis
Latent variable methods
Machine learning
Molecular epidemiology
Nonparametric Bayes
Order restricted inference
Model selection and averaging


Action Potentials • Algorithms • Artificial Intelligence • B-Lymphocytes • Bayes Theorem • Computer Simulation • Data Interpretation, Statistical • DNA-Binding Proteins • Electrophysiological Phenomena • Epidemiologic Methods • Gene Dosage • Genetic Association Studies • Germinal Center • Longitudinal Studies • Markov Chains • Models, Statistical • Models, Theoretical • Monte Carlo Method • Multivariate Analysis • Neurons • Pattern Recognition, Automated • Phenotype • Probability • Stochastic differential equations

Representative Publications   (search)

  1. Dunson, DB, Nonparametric Bayes local partition models for random effects., Biometrika, vol. 96 no. 2 (2009), pp. 249-262, ISSN 0006-3444 [doi]  [abs]
  2. Bigelow, JL; Dunson, DB, Bayesian semiparametric joint models for functional predictors, Journal of the American Statistical Association, vol. 104 no. 485 (2009), pp. 26-36, ISSN 0162-1459 [doi]  [abs]
  3. Dunson, DB; Xing, C, Nonparametric Bayes Modeling of Multivariate Categorical Data., Journal of the American Statistical Association, vol. 104 no. 487 (2009), pp. 1042-1051, ISSN 0162-1459 [doi]  [abs]
  4. Dunson, DB; Park, J-H, Kernel stick-breaking processes, Biometrika, vol. 95 no. 2 (2008), pp. 307-323, ISSN 0006-3444 [doi]  [abs]
  5. Dunson, DB; Herring, AH; Engel, SM, Bayesian selection and clustering of polymorphisms in functionally related genes, Journal of the American Statistical Association, vol. 103 no. 482 (2008), pp. 534-546, ISSN 0162-1459 [doi]  [abs]
Recent Grant Support