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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This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (No. R01-2008-000-10133-0).
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Kim, J., Cheon, S. Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo. Comput Stat 25, 215–239 (2010). https://doi.org/10.1007/s00180-009-0172-x
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DOI: https://doi.org/10.1007/s00180-009-0172-x