Dr Georgios Karagiannis georgios.karagiannis@durham.ac.uk
Associate Professor
Annealed Importance Sampling Reversible Jump MCMC Algorithms
Karagiannis, G.; Andrieu, C.
Authors
C. Andrieu
Abstract
We develop a methodology to efficiently implement the reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithms of Green, applicable for example to model selection inference in a Bayesian framework, which builds on the “dragging fast variables” ideas of Neal. We call such algorithms annealed importance sampling reversible jump (aisRJ). The proposed procedures can be thought of as being exact approximations of idealized RJ algorithms which in a model selection problem would sample the model labels only, but cannot be implemented. Central to the methodology is the idea of bridging different models with fictitious intermediate models, whose role is to introduce smooth intermodel transitions and, as we shall see, improve performance. Efficiency of the resulting algorithms is demonstrated on two standard model selection problems and we show that despite the additional computational effort incurred, the approach can be highly competitive computationally. Supplementary materials for the article are available online.
Citation
Karagiannis, G., & Andrieu, C. (2013). Annealed Importance Sampling Reversible Jump MCMC Algorithms. Journal of Computational and Graphical Statistics, 22(3), 623-648. https://doi.org/10.1080/10618600.2013.805651
Journal Article Type | Article |
---|---|
Online Publication Date | Sep 20, 2013 |
Publication Date | Sep 20, 2013 |
Deposit Date | Nov 10, 2016 |
Publicly Available Date | Aug 22, 2017 |
Journal | Journal of Computational and Graphical Statistics |
Print ISSN | 1061-8600 |
Electronic ISSN | 1537-2715 |
Publisher | American Statistical Association |
Peer Reviewed | Peer Reviewed |
Volume | 22 |
Issue | 3 |
Pages | 623-648 |
DOI | https://doi.org/10.1080/10618600.2013.805651 |
Public URL | https://durham-repository.worktribe.com/output/1393927 |
Files
Accepted Journal Article
(2.3 Mb)
PDF
Copyright Statement
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computational and Graphical Statistics on 20/09/2013, available online: http://www.tandfonline.com/10.1080/10618600.2013.805651.
You might also like
Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs
(2021)
Presentation / Conference Contribution
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
(2021)
Presentation / Conference Contribution
Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes
(2018)
Presentation / Conference Contribution
ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets
(2019)
Presentation / Conference Contribution
Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation
(2019)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search