Bayesian analysis of change point problems using conditionally specified priors

Shahtahmassebi, G ORCID: https://orcid.org/0000-0002-0630-2750 and Sarabia, JM, 2023. Bayesian analysis of change point problems using conditionally specified priors. Annals of Data Science. ISSN 2198-5804

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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:	Number Type 10.1007/s40745-023-00484-2 DOI 1870188 Other
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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