Posterior consistency in conditional distribution estimation.

Journal Article (Journal Article)

A wide variety of priors have been proposed for nonparametric Bayesian estimation of conditional distributions, and there is a clear need for theorems providing conditions on the prior for large support, as well as posterior consistency. Estimation of an uncountable collection of conditional distributions across different regions of the predictor space is a challenging problem, which differs in some important ways from density and mean regression estimation problems. Defining various topologies on the space of conditional distributions, we provide sufficient conditions for posterior consistency focusing on a broad class of priors formulated as predictor-dependent mixtures of Gaussian kernels. This theory is illustrated by showing that the conditions are satisfied for a class of generalized stick-breaking process mixtures in which the stick-breaking lengths are monotone, differentiable functions of a continuous stochastic process. We also provide a set of sufficient conditions for the case where stick-breaking lengths are predictor independent, such as those arising from a fixed Dirichlet process prior.

Full Text

Duke Authors

Cited Authors

  • Pati, D; Dunson, DB; Tokdar, ST

Published Date

  • April 2013

Published In

Volume / Issue

  • 116 /

Start / End Page

  • 456 - 472

PubMed ID

  • 25067858

Pubmed Central ID

  • PMC4107341

International Standard Serial Number (ISSN)

  • 0047-259X

Digital Object Identifier (DOI)

  • 10.1016/j.jmva.2013.01.011


  • eng