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Shahtahmassebi, G. 
ORCID: 0000-0002-0630-2750 and Sarabia, J.M.,
2023.
Bayesian analysis of change point problems using conditionally specified priors.
Annals of Data Science.
ISSN 2198-5804
Abstract
In data analysis, change point problems correspond to abrupt changes in stochastic mechanisms generating data. The detection of change points is a relevant problem in the analysis and prediction of time series. In this paper, we consider a class of conjugate prior distributions obtained from conditional specification methodology for solving this problem. We illustrate the application of such distributions in Bayesian change point detection analysis with Poisson processes. We obtain the posterior distribution of model parameters using general bivariate distribution with gamma conditionals. Simulation from the posterior are readily implemented using a Gibbs sampling algorithm. The Gibbs sampling is implemented even when using conditional densities that are incompatible or only compatible with an improper joint density. The application of such methods will be demonstrated using examples of simulated and real data.
| Item Type: | Journal article | ||||||
|---|---|---|---|---|---|---|---|
| Publication Title: | Annals of Data Science | ||||||
| Creators: | Shahtahmassebi, G. and Sarabia, J.M. | ||||||
| Publisher: | Springer Science and Business Media LLC | ||||||
| Date: | 8 August 2023 | ||||||
| ISSN: | 2198-5804 | ||||||
| Identifiers: |
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| Rights: | . | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Jeremy Silvester | ||||||
| Date Added: | 07 Mar 2024 16:04 | ||||||
| Last Modified: | 07 Mar 2024 16:04 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/51017 |
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