  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.

Methodology