Bayesian nonparametrics for stochastic epidemic models
The vast majority of models for the spread of communicable diseases are parametric in nature and involve underlying assumptions about how the disease spreads through a population. In this article we consider the use of Bayesian nonparametric approaches to analysing data from disease outbreaks. Speci...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Published: |
Institute of Mathematical Statistics
2018
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/43486/ |
| _version_ | 1848796699662745600 |
|---|---|
| author | Kypraios, Theodore O'Neill, Philip D. |
| author_facet | Kypraios, Theodore O'Neill, Philip D. |
| author_sort | Kypraios, Theodore |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The vast majority of models for the spread of communicable diseases are parametric in nature and involve underlying assumptions about how the disease spreads through a population. In this article we consider the use of Bayesian nonparametric approaches to analysing data from disease outbreaks. Specifically we focus on methods for estimating the infection process in simple models under the assumption that this process has an explicit time-dependence. |
| first_indexed | 2025-11-14T19:52:08Z |
| format | Article |
| id | nottingham-43486 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:52:08Z |
| publishDate | 2018 |
| publisher | Institute of Mathematical Statistics |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-434862020-05-04T19:29:42Z https://eprints.nottingham.ac.uk/43486/ Bayesian nonparametrics for stochastic epidemic models Kypraios, Theodore O'Neill, Philip D. The vast majority of models for the spread of communicable diseases are parametric in nature and involve underlying assumptions about how the disease spreads through a population. In this article we consider the use of Bayesian nonparametric approaches to analysing data from disease outbreaks. Specifically we focus on methods for estimating the infection process in simple models under the assumption that this process has an explicit time-dependence. Institute of Mathematical Statistics 2018-02-02 Article PeerReviewed Kypraios, Theodore and O'Neill, Philip D. (2018) Bayesian nonparametrics for stochastic epidemic models. Statistical Science, 33 (1). pp. 44-56. ISSN 2168-8745 Bayesian nonparametrics Epidemic model Gaussian process https://projecteuclid.org/euclid.ss/1517562024 doi:10.1214/17-STS617 doi:10.1214/17-STS617 |
| spellingShingle | Bayesian nonparametrics Epidemic model Gaussian process Kypraios, Theodore O'Neill, Philip D. Bayesian nonparametrics for stochastic epidemic models |
| title | Bayesian nonparametrics for stochastic epidemic models |
| title_full | Bayesian nonparametrics for stochastic epidemic models |
| title_fullStr | Bayesian nonparametrics for stochastic epidemic models |
| title_full_unstemmed | Bayesian nonparametrics for stochastic epidemic models |
| title_short | Bayesian nonparametrics for stochastic epidemic models |
| title_sort | bayesian nonparametrics for stochastic epidemic models |
| topic | Bayesian nonparametrics Epidemic model Gaussian process |
| url | https://eprints.nottingham.ac.uk/43486/ https://eprints.nottingham.ac.uk/43486/ https://eprints.nottingham.ac.uk/43486/ |