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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 | ||||||
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| Publication Title: | Annals of Data Science | ||||||
| Creators: | Shahtahmassebi, G. and Sarabia, J.M. | ||||||
| Publisher: | Springer | ||||||
| Date: | 8 August 2023 | ||||||
| ISSN: | 2198-5804 | ||||||
| Identifiers: |
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| Rights: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Jonathan Gallacher | ||||||
| Date Added: | 03 Jun 2024 09:20 | ||||||
| Last Modified: | 03 Jun 2024 09:20 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/51513 |
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