PHILIP O'NEILL PHILIP.ONEILL@NOTTINGHAM.AC.UK
Professor of Applied Probability
Bayesian model choice via mixture distributions with application to epidemics and population process models
O'Neill, Philip D.; Kypraios, Theodore
Authors
Dr THEODORE KYPRAIOS THEODORE.KYPRAIOS@NOTTINGHAM.AC.UK
Professor of Statistics
Abstract
We consider Bayesian model choice for the setting where the observed data are partially observed realisations of a stochastic population process. A new method for computing Bayes factors is described which avoids the need to use reversible jump approaches. The key idea is to perform inference for a hypermodel in which the competing models are components of a mixture distribution. The method itself has fairly general applicability. The methods are illustrated using simple population process models and stochastic epidemics.
Citation
O'Neill, P. D., & Kypraios, T. Bayesian model choice via mixture distributions with application to epidemics and population process models. University of Nottingham
Book Type | Monograph |
---|---|
Deposit Date | Nov 28, 2014 |
Peer Reviewed | Not Peer Reviewed |
Public URL | https://nottingham-repository.worktribe.com/output/999463 |
Files
ONeill_Kypraios_mixtures.pdf
(141 Kb)
PDF
You might also like
Bayesian nonparametric inference for heterogeneously mixing infectious disease models
(2022)
Journal Article
Pair-based likelihood approximations for stochastic epidemic models
(2019)
Journal Article
Bayes Factors for Partially Observed Stochastic Epidemic Models
(2018)
Journal Article
Bayesian nonparametrics for stochastic epidemic models
(2018)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: digital-library-support@nottingham.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