Options
Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods
Author(s)
Date Issued
2015
Date Available
2015-08-05T16:24:56Z
Abstract
Many popular statistical models for complex phenomena areintractable, in the sense that the likelihood function cannot easily be evaluated.Bayesian estimation in this setting remains challenging, with a lack of computa-tional methodology to fully exploit modern processing capabilities. In this paperwe introduce novel control variates for intractable likelihoods that can dramati-cally reduce the Monte Carlo variance of Bayesian estimators. We prove that ourcontrol variates are well-defined and provide a positive variance reduction. Fur-thermore, we show how to optimise these control variates for variance reduction.The methodology is highly parallel and offers a route to exploit multi-core pro-cessing architectures that complements recent research in this direction. Indeed,our work shows that it may not be necessary to parallelise the sampling processitself in order to harness the potential of massively multi-core architectures. Simu-lation results presented on the Ising model, exponential random graph models andnon-linear stochastic differential equation models support our theoretical findings.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Swiss National Science Foundation
EPSRC Centre for Research in Statistical Methodology
Type of Material
Journal Article
Publisher
International Society for Bayesian Analysis (ISBA)
Journal
Bayesian Analysis
Volume
11
Issue
1
Start Page
1
End Page
31
Copyright (Published Version)
2015 International Society for Bayesian Analysis
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
File(s)
Owning collection
Scopus© citations
13
Acquisition Date
Mar 19, 2024
Mar 19, 2024
Views
1544
Acquisition Date
Mar 19, 2024
Mar 19, 2024
Downloads
335
Last Week
2
2
Last Month
4
4
Acquisition Date
Mar 19, 2024
Mar 19, 2024