Optimal scaling of the independence sampler:theory and practice

Lee, Clement and Neal, Peter John (2018) Optimal scaling of the independence sampler:theory and practice. Bernoulli, 24 (3). pp. 1636-1652. ISSN 1350-7265

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Abstract

The independence sampler is one of the most commonly used MCMC algorithms usually as a component of a Metropolis-within-Gibbs algorithm. The common focus for the independence sampler is on the choice of proposal distribution to obtain an as high as possible acceptance rate. In this paper we have a somewhat different focus concentrating on the use of the independence sampler for updating augmented data in a Bayesian framework where a natural proposal distribution for the independence sampler exists. Thus we concentrate on the proportion of the augmented data to update to optimise the independence sampler. Generic guidelines for optimising the independence sampler are obtained for independent and identically distributed product densities mirroring findings for the random walk Metropolis algorithm. The generic guidelines are shown to be informative beyond the narrow confines of idealised product densities in two epidemic examples.

Item Type:
Journal Article
Journal or Publication Title:
Bernoulli
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
81879
Deposited By:
Deposited On:
05 Oct 2016 10:06
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Apr 2020 04:12