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

被引：39

作者：

Kim, Jaehee ^{[1
]}

Cheon, Sooyoung ^{[2
]}

机构：

[1] Duksung Womens Univ, Dept Stat, Seoul 132714, South Korea

[2] Korea Univ, Dept Informat Stat, Jochiwon 339700, South Korea

来源：

COMPUTATIONAL STATISTICS | 2010年 / 25卷 / 02期

关键词：

Annealing Stochastic Approximation Monte Carlo (ASAMC); Bayesian change-point model; Bayes factor; BIC; Posterior; Truncated Poisson; RANDOM-VARIABLES; INFERENCE; MODELS; TIME; DISTRIBUTIONS; COMPUTATION; EFFICIENT; ALGORITHM; POLLUTION; SEQUENCE;

D O I：

10.1007/s00180-009-0172-x

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：215 / 239

页数：25

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