Geodesic
Application of MCMC to change point detection
Applications of Mathematics, Tome 53 (2008) no. 4, pp. 281-296 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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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.
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.
DOI : 10.1007/s10492-008-0026-9
Classification : 62F40, 62P12, 65C05, 65C40, 65C60, 86A10
Keywords: change point estimation; Markov chain Monte Carlo (MCMC); Metropolis-Hastings algorithm; Gibbs sampler; Bayesian statistics; Klementinum temperature series 
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     author = {Antoch, Jarom{\'\i}r and Leg\'at, David},
     title = {Application of {MCMC} to change point detection},
     journal = {Applications of Mathematics},
     pages = {281--296},
     year = {2008},
     volume = {53},
     number = {4},
     doi = {10.1007/s10492-008-0026-9},
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     zbl = {1199.65016},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1007/s10492-008-0026-9/}
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Antoch, Jaromír; Legát, David. Application of MCMC to change point detection. Applications of Mathematics, Tome 53 (2008) no. 4, pp. 281-296. doi: 10.1007/s10492-008-0026-9

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