Bayesian Variable Selection via Particle Stochastic Search.

Published

Journal Article

We focus on Bayesian variable selection in regression models. One challenge is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In this article, we propose a new computational approach based on sequential Monte Carlo (SMC), which we refer to as particle stochastic search (PSS). We illustrate PSS through applications to linear regression and probit models.

Full Text

Duke Authors

Cited Authors

  • Shi, M; Dunson, DB

Published Date

  • February 2011

Published In

Volume / Issue

  • 81 / 2

Start / End Page

  • 283 - 291

PubMed ID

  • 21278860

Pubmed Central ID

  • 21278860

International Standard Serial Number (ISSN)

  • 0167-7152

Digital Object Identifier (DOI)

  • 10.1016/j.spl.2010.10.011

Language

  • eng