Home > Research > Publications & Outputs > An efficient Markov chain Monte Carlo method fo...

Research

Links

Text available via DOI:

Keywords

View graph of relations

An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants.

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

Published

Standard

An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. / Pettitt, Anthony; Berthelsen, K.; Moller, J. et al.
In: Biometrika, Vol. 93, No. 2, 01.06.2006, p. 451-458.

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

Harvard

Pettitt, A, Berthelsen, K, Moller, J & Reeves, R 2006, 'An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants.', Biometrika, vol. 93, no. 2, pp. 451-458. https://doi.org/10.1093/biomet/93.2.451

APA

Pettitt, A., Berthelsen, K., Moller, J., & Reeves, R. (2006). An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika, 93(2), 451-458. https://doi.org/10.1093/biomet/93.2.451

Vancouver

Pettitt A, Berthelsen K, Moller J, Reeves R. An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika. 2006 Jun 1;93(2):451-458. doi: 10.1093/biomet/93.2.451

Author

Pettitt, Anthony ; Berthelsen, K. ; Moller, J. et al. / An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. In: Biometrika. 2006 ; Vol. 93, No. 2. pp. 451-458.

Bibtex

@article{7a8866db6ff747509f38af2e90c55d27,
title = "An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants.",
abstract = "Maximum likelihood parameter estimation and sampling from Bayesian posterior distributions are problematic when the probability density for the parameter of interest involves an intractable normalising constant which is also a function of that parameter. In this paper, an auxiliary variable method is presented which requires only that independent samples can be drawn from the unnormalised density at any particular parameter value. The proposal distribution is constructed so that the normalising constant cancels from the Metropolis-Hastings ratio. The method is illustrated by producing posterior samples for parameters of the Ising model given a particular lattice realisation.",
keywords = "Auxiliary variable method, Ising model, Markov chain Monte Carlo, Metropolis-Hastings algorithm, Normalising constant, Partition function",
author = "Anthony Pettitt and K. Berthelsen and J. Moller and R. Reeves",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2006",
month = jun,
day = "1",
doi = "10.1093/biomet/93.2.451",
language = "English",
volume = "93",
pages = "451--458",
journal = "Biometrika",
issn = "1464-3510",
publisher = "Oxford University Press",
number = "2",

}

RIS

TY - JOUR

T1 - An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants.

AU - Pettitt, Anthony

AU - Berthelsen, K.

AU - Moller, J.

AU - Reeves, R.

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2006/6/1

Y1 - 2006/6/1

N2 - Maximum likelihood parameter estimation and sampling from Bayesian posterior distributions are problematic when the probability density for the parameter of interest involves an intractable normalising constant which is also a function of that parameter. In this paper, an auxiliary variable method is presented which requires only that independent samples can be drawn from the unnormalised density at any particular parameter value. The proposal distribution is constructed so that the normalising constant cancels from the Metropolis-Hastings ratio. The method is illustrated by producing posterior samples for parameters of the Ising model given a particular lattice realisation.

AB - Maximum likelihood parameter estimation and sampling from Bayesian posterior distributions are problematic when the probability density for the parameter of interest involves an intractable normalising constant which is also a function of that parameter. In this paper, an auxiliary variable method is presented which requires only that independent samples can be drawn from the unnormalised density at any particular parameter value. The proposal distribution is constructed so that the normalising constant cancels from the Metropolis-Hastings ratio. The method is illustrated by producing posterior samples for parameters of the Ising model given a particular lattice realisation.

KW - Auxiliary variable method

KW - Ising model

KW - Markov chain Monte Carlo

KW - Metropolis-Hastings algorithm

KW - Normalising constant

KW - Partition function

U2 - 10.1093/biomet/93.2.451

DO - 10.1093/biomet/93.2.451

M3 - Journal article

VL - 93

SP - 451

EP - 458

JO - Biometrika

JF - Biometrika

SN - 1464-3510

IS - 2

ER -