Bayesian Analysis of Change Point Problems Using Conditionally Specified Priors

G. Shahtahmassebi () and José María Sarabia
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G. Shahtahmassebi: Nottingham Trent University
José María Sarabia: CUNEF Universidad

Annals of Data Science, 2024, vol. 11, issue 6, No 2, 1899-1918

Abstract: 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.

Keywords: Conditional specification; Change point; Bayesian analysis; Gamma conditional distributions (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-023-00484-2

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