Bayesian analysis of change point problems for time series data

Abstract

Abstract. Alterations or sudden changes within a sequence of temporal observations always create disturbance to data analysis. The maneuver to detect this alterations or changes in any temporal data may allow researchers to identify the aberration in every block of segments. The Bayesian method proposed by Barry and Hartigan has greatly fitted the analysis of change point problems through product partition model. We study Bayesian analysis for change point problem with Markov sampling computation on British coal mine accident. The result provides accurate change point and posterior means estimation.

Phases 1 and 2 of Covid-19 Epidemic in the Three Geographical Areas of Italy: An Estimation of Italian Government Measures Based on a Bayesian Changepoint Detection Method

“…We have adopted a Bayesian changepoint detection methodology, originally proposed by Barry and Hartigan (BH) [5], based on Markov Chain Monte Carlo (MCMC) methods. A similar procedure has already been used, for example, by Lee and Ong [6], or Gregori and coworkers [7]. In the present work, BH approach was utilized to detect when the epidemic changed speed as compared with what was to be expected.…”