Issue No. 02 - Feb. (2015 vol. 37)
Nicholas J. Foti , Statistics Department, University of Washington, Seattle, WA, USA
Sinead A. Williamson , Department of InformationRisk and Operations Management, University of Texas at Austin, Austin, TX, USA
Dependent nonparametric processes extend distributions over measures, such as the Dirichlet process and the beta process, to give distributions over collections of measures, typically indexed by values in some covariate space. Such models are appropriate priors when exchangeability assumptions do not hold, and instead we want our model to vary fluidly with some set of covariates. Since the concept of dependent nonparametric processes was formalized by MacEachern, there have been a number of models proposed and used in the statistics and machine learning literatures. Many of these models exhibit underlying similarities, an understanding of which, we hope, will help in selecting an appropriate prior, developing new models, and leveraging inference techniques.
Stochastic processes, Bayes methods, Machine learning,Stochastic processes, Introductory and Survey,dependent Dirichlet processes, Bayesian nonparametrics, non-exchangeable data, dependent stochastic processes
Nicholas J. Foti, Sinead A. Williamson, "A Survey of Non-Exchangeable Priors for Bayesian Nonparametric Models", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 37, no. , pp. 359-371, Feb. 2015, doi:10.1109/TPAMI.2013.224