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Computing the likelihood of sequence segmentation under Markov modelling

Laurent Guéguen 1, * 
* Corresponding author
Abstract : I tackle the problem of partitioning a sequence into homogeneous segments, where homogeneity is defined by a set of Markov models. The problem is to study the likelihood that a sequence is divided into a given number of segments. Here, the moments of this likelihood are computed through an efficient algorithm. Unlike methods involving Hidden Markov Models, this algorithm does not require probability transitions between the models. Among many possible usages of the likelihood, I present a maximum \textit{a posteriori} probability criterion to predict the number of homogeneous segments into which a sequence can be divided, and an application of this method to find CpG islands.
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Submitted on : Monday, November 16, 2009 - 1:17:10 PM
Last modification on : Tuesday, July 20, 2021 - 5:20:05 PM
Long-term archiving on: : Thursday, June 17, 2010 - 8:29:18 PM


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  • HAL Id : hal-00432383, version 1
  • ARXIV : 0911.3070



Laurent Guéguen. Computing the likelihood of sequence segmentation under Markov modelling. 2009. ⟨hal-00432383⟩



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