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Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo

Authors
Kim, JaeheeCheon, Sooyoung
Issue Date
6월-2010
Publisher
SPRINGER HEIDELBERG
Keywords
Annealing Stochastic Approximation Monte Carlo (ASAMC); Bayesian change-point model; Bayes factor; BIC; Posterior; Truncated Poisson
Citation
COMPUTATIONAL STATISTICS, v.25, no.2, pp.215 - 239
Indexed
SCIE
SCOPUS
Journal Title
COMPUTATIONAL STATISTICS
Volume
25
Number
2
Start Page
215
End Page
239
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/116366
DOI
10.1007/s00180-009-0172-x
ISSN
0943-4062
Abstract
Bayesian multiple change-point models are built with data from normal, exponential, binomial and Poisson distributions with a truncated Poisson prior for the number of change-points and conjugate prior for the distributional parameters. We applied Annealing Stochastic Approximation Monte Carlo (ASAMC) for posterior probability calculations for the possible set of change-points. The proposed methods are studied in simulation and applied to temperature and the number of respiratory deaths in Seoul, South Korea.
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