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用碎片-凝固过程模拟遗传变异

Modelling Genetic Variations with Fragmentation-Coagulation Processes
课程网址: http://videolectures.net/nips2011_teh_fragmentation/  
主讲教师: Yee Whye Teh
开课单位: 牛津大学
开课时间: 2012-01-25
课程语种: 英语
中文简介:
我们提出了一类新的贝叶斯非参数模型用于序列数据,称为碎裂凝固过程(FCPs)。 FCP使用分区值马尔可夫过程对一组序列建模,该过程通过分裂和合并群集而发展。 FCP是可交换的,投射的,静止的和可逆的,其均衡分布由中国餐馆过程给出。与隐马尔可夫模型相反,FCP允许灵活地建模群集的数量,并且它们避免了标签切换不可识别性问题。我们为FCP开发了一种高效的Gibbs采样器,它使用均匀化和前向后向算法。我们对FCPs的开发受到群体遗传学应用的推动,并且我们证明了FCP对基因型插补问题的实用性,其中相位和非相位SNP数据。
课程简介: We propose a novel class of Bayesian nonparametric models for sequential data called fragmentation-coagulation processes (FCPs). FCPs model a set of sequences using a partition-valued Markov process which evolves by splitting and merging clusters. An FCP is exchangeable, projective, stationary and reversible, and its equilibrium distributions are given by the Chinese restaurant process. As opposed to hidden Markov models, FCPs allow for flexible modelling of the number of clusters, and they avoid label switching non-identifiability problems. We develop an efficient Gibbs sampler for FCPs which uses uniformization and the forward-backward algorithm. Our development of FCPs is motivated by applications in population genetics, and we demonstrate the utility of FCPs on problems of genotype imputation with phased and unphased SNP data.
关 键 词: 贝叶斯非参数模型; 碎裂凝固过程; 马尔可夫过程
课程来源: 视频讲座网
最后编审: 2019-09-06:lxf
阅读次数: 39