Abstract To relax the homogeneity assumption of dynamic bayesian networks (DBNs), the non-homogeneous DBNs is proposed. In this paper, an improved reversible-jump Markov chain Monte Carlo (RJ-MCMC) algorithm is put forward by integrating the prior knowledge about the sliding window, namely APK-RJ-MCMC. The basic assumption of APK-RJ-MCMC is that the bigger the distribution distance between the backward window and the forward window of a time point is, the higher the probability of the time point as a changepoint becomes. Based on the above assumption, the rough probability of each time point as a changepoint is obtained. And it is considered as prior knowledge to guide birth, death and shift moves in RJ-MCMC algorithm during the changepoint sampling. Finally, the accept probability is thus adjusted. Experimental results on both the synthetic data and the real gene expression data show that the proposed APK-RJ-MCMC has a higher posterior probability and better AUC scores than the traditional algorithm does in changepoint detection.
Fund:Supported by key Program of National Natural Science Foundation of China (No.61432008), Young Scientists Fund of National Natural Science Foundation of China (No.61305068)
About author:: (YU Lu, born in 1992, master student. Her research interests include probabilistic graphic model.)(GAO Yang(Corresponding author), born in 1972, Ph.D., professor. His research interests include artificial intelligence and machine learning.)(SHI Yinghuan, born in 1984, Ph.D., associate professor. His research interests include medical image analysis and machine vision.)
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