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Bayesian hidden Markov Model for DNA segmentation : A prior sensitivity analysis

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Date
2009
Link to item file
http://hal.archives-ouvertes.fr/hal-00328181/en/
Dewey
Probabilités et mathématiques appliquées
Sujet
DNA sequence; hidden Markov model; Bayesian model; sensitivity analysis; α-fetoprotein; Markov chain Monte Carlo; importance sampling.
Journal issue
Computational Statistics and Data Analysis
Volume
53
Number
5
Publication date
03-2009
Article pages
1873-1882
DOI
http://dx.doi.org/10.1016/j.csda.2008.07.007
URI
https://basepub.dauphine.fr/handle/123456789/501
Collections
  • CEREMADE : Publications
Metadata
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Author
Nur, Darfiana
Allingham, David
Rousseau, Judith
Mengersen, Kerrie
McVinish, Ross
Type
Article accepté pour publication ou publié
Abstract (EN)
The focus of this paper is on the sensitivity to the specification of the prior in a hidden Markov model describing homogeneous segments of DNA sequences. An intron from the chimpanzee α-fetoprotein gene, which plays an im- portant role in embryonic development in mammals is analysed. Three main aims are considered : (i) to assess the sensitivity to prior specification in Bayesian hidden Markov models for DNA sequence segmentation; (ii) to examine the impact of replacing the standard Dirichlet prior with a mixture Dirichlet prior; and (iii) to propose and illus- trate a more comprehensive approach to sensitivity analysis, using importance sampling. It is obtained that (i) the posterior estimates obtained under a Bayesian hidden Markov model are indeed sensitive to the specification of the prior distributions; (ii) compared with the standard Dirichlet prior, the mixture Dirichlet prior is more flexible, less sensitive to the choice of hyperparameters and less constraining in the analysis, thus improving posterior estimates; and (iii) importance sampling was computationally feasible, fast and effective in allowing a richer sensitivity analysis.

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