Journal article icon

Journal article

A nonparametric HMM for genetic imputation and coalescent inference

Abstract:

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. Ou...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

Actions


Access Document


Files:
Publisher copy:
10.1214/16-EJS1197

Authors


More by this author
Department:
Oxford, MPLS, Statistics
More by this author
Department:
Oxford, MPLS, Statistics
Gatsby Charitable Foundation More from this funder
Publisher:
Institute of Mathematical Statistics Publisher's website
Journal:
Electronic Journal of Statistics Journal website
Volume:
10
Issue:
2
Pages:
3425-3451
Publication date:
2016-11-05
Acceptance date:
2016-09-18
DOI:
ISSN:
1935-7524
Pubs id:
pubs:653695
URN:
uri:e4de21d5-e80b-469e-b285-79d8970856d7
UUID:
uuid:e4de21d5-e80b-469e-b285-79d8970856d7
Local pid:
pubs:653695

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP