Block updating in constrained Markov chain Monte Carlo sampling

Merrilee A. Hurn; Håvard Rue; Nuala A. Sheehan

doi:10.1016/s0167-7152(98)90150-x

Block updating in constrained Markov chain Monte Carlo sampling

Merrilee A. Hurn^*, Håvard Rue, Nuala A. Sheehan

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Markov chain Monte Carlo methods are widely used to study highly structured stochastic systems. However, when the system is subject to constraints, it is difficult to find irreducible proposal distributions. We suggest a "block-wise" approach for constrained sampling and optimisation.

Original language	English (US)
Pages (from-to)	353-361
Number of pages	9
Journal	Statistics and Probability Letters
Volume	41
Issue number	2
DOIs	https://doi.org/10.1016/s0167-7152(98)90150-x
State	Published - Feb 15 1999
Externally published	Yes

Keywords

Constrained distributions
Importance sampling
Irreducibility
Markov chain monte carlo
Multiple-site updating
Stochastic simulation and optimisation

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/s0167-7152(98)90150-x

Cite this

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title = "Block updating in constrained Markov chain Monte Carlo sampling",

abstract = "Markov chain Monte Carlo methods are widely used to study highly structured stochastic systems. However, when the system is subject to constraints, it is difficult to find irreducible proposal distributions. We suggest a {"}block-wise{"} approach for constrained sampling and optimisation.",

keywords = "Constrained distributions, Importance sampling, Irreducibility, Markov chain monte carlo, Multiple-site updating, Stochastic simulation and optimisation",

author = "Hurn, {Merrilee A.} and H{\aa}vard Rue and Sheehan, {Nuala A.}",

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T1 - Block updating in constrained Markov chain Monte Carlo sampling

AU - Hurn, Merrilee A.

AU - Rue, Håvard

AU - Sheehan, Nuala A.

PY - 1999/2/15

Y1 - 1999/2/15

N2 - Markov chain Monte Carlo methods are widely used to study highly structured stochastic systems. However, when the system is subject to constraints, it is difficult to find irreducible proposal distributions. We suggest a "block-wise" approach for constrained sampling and optimisation.

AB - Markov chain Monte Carlo methods are widely used to study highly structured stochastic systems. However, when the system is subject to constraints, it is difficult to find irreducible proposal distributions. We suggest a "block-wise" approach for constrained sampling and optimisation.

KW - Constrained distributions

KW - Importance sampling

KW - Irreducibility

KW - Markov chain monte carlo

KW - Multiple-site updating

KW - Stochastic simulation and optimisation

UR - http://www.scopus.com/inward/record.url?scp=0033556955&partnerID=8YFLogxK

U2 - 10.1016/s0167-7152(98)90150-x

DO - 10.1016/s0167-7152(98)90150-x

M3 - Article

AN - SCOPUS:0033556955

SN - 0167-7152

VL - 41

SP - 353

EP - 361

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 2

ER -

Block updating in constrained Markov chain Monte Carlo sampling

Abstract

Keywords

ASJC Scopus subject areas

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