# Variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations

arXiv: https://arxiv.org/abs/1706.06742 · HTML: https://mlsanity.com/paper/1706.06742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series](https://mlsanity.com/paper/2311.07867/) (2023)

## Abstract (arXiv)

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.