CONVERGENCE RATE OF MARKOV CHAIN METHODS FOR GENOMIC MOTIF DISCOVERY

• 成果类型: Article
• 署名作者: Woodard, Dawn B.; Rosenthal, Jeffrey S.
• 署名单位: Cornell University; Cornell University; University of Toronto
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/12-AOS1075
• 发表日期: 2013
• 页码: 91-124
• 关键词: monte-carlo; GIBBS SAMPLER; geometric ergodicity; distributions; MODEL; parallel

摘要

We analyze the convergence rate of a simplified version of a popular Gibbs sampling method used for statistical discovery of gene regulatory binding motifs in DNA sequences. This sampler satisfies a very strong form of ergodicity (uniform). However, we show that, due to multimodality of the posterior distribution, the rate of convergence often decreases exponentially as a function of the length of the DNA sequence. Specifically, we show that this occurs whenever there is more than one true repeating pattern in the data. In practice there are typically multiple such patterns in biological data, the goal being to detect the most well-conserved and frequently-occurring of these. Our findings match empirical results, in which the motif-discovery Gibbs sampler has exhibited such poor convergence that it is used only for finding modes of the posterior distribution (candidate motifs) rather than for obtaining samples from that distribution. Ours are some of the first meaningful bounds on the convergence rate of a Markov chain method for sampling from a multimodal posterior distribution, as a function of statistical quantities like the number of observations.