Bayesian Variable Selection via Particle Stochastic Search |
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Authors: | Shi Minghui Dunson David B |
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Institution: | Department of Statistical Science, Box 90251, Duke University, Durham, NC, 27708, USA |
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Abstract: | 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. |
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Keywords: | Bayes factor Marginal inclusion probability Model averaging Model uncertainty Sequential Monte Carlo |
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