Bayesian model choice via mixture distributions with application to epidemics and population process models
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...
| Main Authors: | , |
|---|---|
| Format: | Monograph |
| Published: |
University of Nottingham
2014
|
| Online Access: | https://eprints.nottingham.ac.uk/27861/ |
| _version_ | 1848793458051907584 |
|---|---|
| author | O'Neill, Philip D. Kypraios, Theodore |
| author_facet | O'Neill, Philip D. Kypraios, Theodore |
| author_sort | O'Neill, Philip D. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:00:37Z |
| format | Monograph |
| id | nottingham-27861 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:00:37Z |
| publishDate | 2014 |
| publisher | University of Nottingham |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-278612020-05-04T20:17:28Z https://eprints.nottingham.ac.uk/27861/ Bayesian model choice via mixture distributions with application to epidemics and population process models O'Neill, Philip D. Kypraios, Theodore 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. University of Nottingham 2014 Monograph NonPeerReviewed O'Neill, Philip D. and Kypraios, Theodore (2014) Bayesian model choice via mixture distributions with application to epidemics and population process models. Working Paper. University of Nottingham. (Unpublished) |
| spellingShingle | O'Neill, Philip D. Kypraios, Theodore Bayesian model choice via mixture distributions with application to epidemics and population process models |
| title | Bayesian model choice via mixture distributions with application to epidemics and population process models |
| title_full | Bayesian model choice via mixture distributions with application to epidemics and population process models |
| title_fullStr | Bayesian model choice via mixture distributions with application to epidemics and population process models |
| title_full_unstemmed | Bayesian model choice via mixture distributions with application to epidemics and population process models |
| title_short | Bayesian model choice via mixture distributions with application to epidemics and population process models |
| title_sort | bayesian model choice via mixture distributions with application to epidemics and population process models |
| url | https://eprints.nottingham.ac.uk/27861/ |