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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제21권 제2호
- 발행연도
- 2019.1
- 수록면
- 559 - 569 (11page)
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초록· 키워드
오류제보하기
In the change point inference of incomplete data, the expectation-maximization (EM) algorithm is often difficult to handle, and thus the Markov chain Monte Carlo (MCMC) method has been used in this area for a long time. However, the traditional MCMC algorithm tends to be trapped to local minima when generating samples from the posterior distribution of change points. To overcome this problem, various advanced Monte Carlo methods have been proposed, but still somewhat difficult to use. This paper proposes an evolutionary Monte Carlo EM (EMCEM) algorithm that combines the evolutionary Monte Carlo algorithm (EMC) with EM using the maximum likelihood method for efficient and user-friendly sampling. EMC has incorporated several attractive features of genetic algorithms and simulated annealing into the framework of MCMC. EMCEM is compared with reversible jump MCMC version of EM (RJMCMCEM), the stochastic approximation version of EM (SAEM) and the stochastic approximation Monte Carlo version of EM (SAMCEM) on simulated and real datasets. The numerical results indicate that EMCEM can outperform RJMCMCEM and SAEM by producing much more accurate parameter estimates, and EMCEM is comparable to SAMCEM.
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참고문헌 (23)
참고문헌 신청
Casella, G. / 1992 / Explaining the Gibbs sampler / The American Statistician 46(3):167~174
전수영 / 2008 / Applications to Bayesian Model Selection Problems via Stochastic Approximation Monte Carlo / Journal of The Korean Data Analysis Society 10(6):3043~3056
전수영 / 2011 / Bayesian Regime-switching Analysis via Stochastic Approximation Monte Carlo / Journal of The Korean Data Analysis Society 13(2):599~609
Delyon, B. / 1999 / Convergence of a stochastic approximation version of the EM algorithm / Annals of Statistics 27:94~128
Dempster, A. P. / 1977 / Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society / Series B 39:1~38
전수영 / 2008 / Applications to Bayesian Model Selection Problems via Stochastic Approximation Monte Carlo / Journal of The Korean Data Analysis Society 10(6):3043~3056
전수영 / 2011 / Bayesian Regime-switching Analysis via Stochastic Approximation Monte Carlo / Journal of The Korean Data Analysis Society 13(2):599~609
Delyon, B. / 1999 / Convergence of a stochastic approximation version of the EM algorithm / Annals of Statistics 27:94~128
Dempster, A. P. / 1977 / Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society / Series B 39:1~38
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