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A nonparametric HMM for genetic imputation and coalescent inference
[article]
2016
arXiv
pre-print
Genetic sequence data are well described by hidden Markov models (HMMs) in which latent states correspond to clusters of similar mutation patterns. Theory from statistical genetics suggests that these HMMs are nonhomogeneous (their transition probabilities vary along the chromosome) and have large support for self transitions. We develop a new nonparametric model of genetic sequence data, based on the hierarchical Dirichlet process, which supports these self transitions and nonhomogeneity. Our
arXiv:1611.00544v1
fatcat:73yv7ivnfffrngnliq5angq2f4