Hierarchical Bayesian nonparametric models with applications [chapter]

Yee Whye Teh, Michael I. Jordan, Nils Lid Hjort, Chris Holmes, Peter Muller, Stephen G. Walker
Bayesian Nonparametrics  
Hierarchical modeling is a fundamental concept in Bayesian statistics. The basic idea is that parameters are endowed with distributions which may themselves introduce new parameters, and this construction recurses. In this review we discuss the role of hierarchical modeling in Bayesian nonparametrics, focusing on models in which the infinite-dimensional parameters are treated hierarchically. For example, we consider a model in which the base measure for a Dirichlet process is itself treated as
more » ... itself treated as a draw from another Dirichlet process. This yields a natural recursion that we refer to as a hierarchical Dirichlet process. We also discuss hierarchies based on the Pitman-Yor process and on completely random processes. We demonstrate the value of these hierarchical constructions in a wide range of practical applications, in problems in computational biology, computer vision and natural language processing.
doi:10.1017/cbo9780511802478.006 fatcat:hmlwr6d3srdsdjkgnvj6f3u4pu