Applications of Mathematics

, Volume 53, Issue 4, pp 281–296 | Cite as

Application of MCMC to change point detection

  • Jaromír AntochEmail author
  • David Legát


A nonstandard approach to change point estimation is presented in this paper. Three models with random coefficients and Bayesian approach are used for modelling the year average temperatures measured in Prague Klementinum. The posterior distribution of the change point and other parameters are estimated from the random samples generated by the combination of the Metropolis-Hastings algorithm and the Gibbs sampler.


change point estimation Markov chain Monte Carlo (MCMC) Metropolis-Hastings algorithm Gibbs sampler Bayesian statistics Klementinum temperature series 


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Copyright information

© Mathematical Institute, Academy of Sciences of Czech Republic 2008

Authors and Affiliations

  1. 1.Faculty of Mathematics and Physics, Department of StatisticsCharles University in PraguePraha 8-KarlínCzech Republic

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