Parallel algorithms for Markov chain Monte Carlo methods in latent spatial Gaussian models

Authors:
Matt Whiley;Simon P. Wilson
Affiliations:
Department of Statistics, Trinity College, Dublin 2, Ireland;Department of Statistics, Trinity College, Dublin 2, Ireland. simon.wilson@tcd.ie
Venue:
Statistics and Computing
Year:
2004

Citing 4
Cited 6

Parallel algorithms for dense linear algebra computations

SIAM Review
Parallel algorithms for sparse linear systems

SIAM Review
ScaLAPACK user's guide

ScaLAPACK user's guide
Developments and trends in the parallel solution of linear systems

Parallel Computing - Special Anniversary issue

Parallel exact sampling and evaluation of Gaussian Markov random fields

Computational Statistics & Data Analysis
Parallelizing MCMC for Bayesian spatiotemporal geostatistical models

Statistics and Computing
Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach

Computational Statistics & Data Analysis
Parallel multivariate slice sampling

Statistics and Computing
Parallel Bayesian inference of range and reflectance from LaDAR profiles

Journal of Parallel and Distributed Computing
On scalability behaviour of Monte Carlo sparse approximate inverse for matrix computations

ScalA '13 Proceedings of the Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Markov chain Monte Carlo (MCMC) implementations of Bayesian inference for latent spatial Gaussian models are very computationally intensive, and restrictions on storage and computation time are limiting their application to large problems. Here we propose various parallel MCMC algorithms for such models. The algorithms' performance is discussed with respect to a simulation study, which demonstrates the increase in speed with which the algorithms explore the posterior distribution as a function of the number of processors. We also discuss how feasible problem size is increased by use of these algorithms.