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Bayesian Analysis of Change Point Detection in Autoregressive Time Series Model with Markov Process States

A. Jafari, M. Yarmohammadi, A. ‎Rasekhi


In this paper, a Bayesian approach for modeling the structural of the multiple change-point detection is introduced. This model has a discrete variable with hidden state which follows a Markov chain process with unknown transition probabilities with beta and logit-normal priors. Then using the appropriate priors for change point model, the posterior distribution are obtained and using simulation studies, the implementation of proposed model will be discussed. Finally an application of this model for index of consumer prices for goods and services in urban areas of Iran (ICPUI) data, is presented.


Beta prior, Change-point, Logit-normal prior, Markov chain, Transition probabilities

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