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Optimal scaling of the independence sampler: theory and practice

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Optimal scaling of the independence sampler: theory and practice. / Lee, Clement; Neal, Peter John.
In: Bernoulli, Vol. 24, No. 3, 02.02.2018, p. 1636-1652.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Lee, C & Neal, PJ 2018, 'Optimal scaling of the independence sampler: theory and practice', Bernoulli, vol. 24, no. 3, pp. 1636-1652. https://doi.org/10.3150/16-BEJ908

APA

Lee, C., & Neal, P. J. (2018). Optimal scaling of the independence sampler: theory and practice. Bernoulli, 24(3), 1636-1652. https://doi.org/10.3150/16-BEJ908

Vancouver

Lee C, Neal PJ. Optimal scaling of the independence sampler: theory and practice. Bernoulli. 2018 Feb 2;24(3):1636-1652. doi: 10.3150/16-BEJ908

Author

Lee, Clement ; Neal, Peter John. / Optimal scaling of the independence sampler : theory and practice. In: Bernoulli. 2018 ; Vol. 24, No. 3. pp. 1636-1652.

Bibtex

@article{2bf143c3436d438b8d5b014b65dd9014,
title = "Optimal scaling of the independence sampler: theory and practice",
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.",
keywords = "Augmented data, Birth-Death-Mutation model, Markov jump process, MCMC, SIR epidemic model",
author = "Clement Lee and Neal, {Peter John}",
year = "2018",
month = feb,
day = "2",
doi = "10.3150/16-BEJ908",
language = "English",
volume = "24",
pages = "1636--1652",
journal = "Bernoulli",
issn = "1350-7265",
publisher = "International Statistical Institute",
number = "3",

}

RIS

TY - JOUR

T1 - Optimal scaling of the independence sampler

T2 - theory and practice

AU - Lee, Clement

AU - Neal, Peter John

PY - 2018/2/2

Y1 - 2018/2/2

N2 - 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.

AB - 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.

KW - Augmented data

KW - Birth-Death-Mutation model

KW - Markov jump process

KW - MCMC

KW - SIR epidemic model

U2 - 10.3150/16-BEJ908

DO - 10.3150/16-BEJ908

M3 - Journal article

VL - 24

SP - 1636

EP - 1652

JO - Bernoulli

JF - Bernoulli

SN - 1350-7265

IS - 3

ER -