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	An MCMC Based EM Algorithm for Mixtures of Gaussian Processes
	Wu, Di ; Chen, Ziyi ; Ma, Jinwen
	2015
关键词	Mixture of Gaussian processes EM algorithm Classification Multimodality Prediction
英文摘要	The mixture of Gaussian processes (MGP) is a powerful statistical learning model for regression and prediction and the EM algorithm is an effective method for its parameter learning or estimation. However, the feasible EM algorithms for MGPs are certain approximations of the real EM algorithm since Q-function cannot be computed efficiently in this situation. To overcome this problem, we propose an MCMC based EM algorithm for MGPs where Q-function is alternatively estimated on a set of simulated samples via the Markov Chain Monte Carlo (MCMC) method. It is demonstrated by the experiments on both the synthetic and real-world datasets that our proposed MCMC based EM algorithm is more effective than the other three EM algorithms for MGPs.; EI; CPCI-S(ISTP); jwma@math.pku.edu.cn; 327-334; 9377
语种	英语
出处	EI ; SCI
出版者	ADVANCES IN NEURAL NETWORKS - ISNN 2015
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/446333]
专题	数学科学学院
推荐引用方式 GB/T 7714	Wu, Di,Chen, Ziyi,Ma, Jinwen. An MCMC Based EM Algorithm for Mixtures of Gaussian Processes. 2015-01-01.

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