Variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations
Methodology
2017-06-22 v1
Abstract
Hidden Markov models provide a natural statistical framework for the detection of the copy number variations (CNV) in genomics. In this paper, we consider a Hidden Markov Model involving several correlated hidden processes at the same time. When dealing with a large number of series, maximum likelihood inference (performed classically using the EM algorithm) becomes intractable. We thus propose an approximate inference algorithm based on a variational approach (VEM). A simulation study is performed to assess the performance of the proposed method and an application to the detection of structural variations in plant genomes is presented.
Keywords
Cite
@article{arxiv.1706.06742,
title = {Variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations},
author = {Xiaoqiang Wang and Emilie Lebarbier and Julie Aubert and Stéphane Robin},
journal= {arXiv preprint arXiv:1706.06742},
year = {2017}
}