Article
MSC:
62F40,
62P12,
65C05,
65C40,
65C60,
86A10 | MR 2433722 | Zbl 1199.65016 | DOI: 10.1007/s10492-008-0026-9
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Keywords:
change point estimation; Markov chain Monte Carlo (MCMC); Metropolis-Hastings algorithm; Gibbs sampler; Bayesian statistics; Klementinum temperature series
change point estimation; Markov chain Monte Carlo (MCMC); Metropolis-Hastings algorithm; Gibbs sampler; Bayesian statistics; Klementinum temperature series
Summary:
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.
References:
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