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

Nur, Darfiana; Allingham, David; Rousseau, Judith; Mengersen, Kerrie; McVinish, Ross (2009), Bayesian hidden Markov Model for DNA segmentation : A prior sensitivity analysis, Computational Statistics and Data Analysis, 53, 5, p. 1873-1882. http://dx.doi.org/10.1016/j.csda.2008.07.007

Type
Article accepté pour publication ou publié
External document link
http://hal.archives-ouvertes.fr/hal-00328181/en/
Date
2009
Journal name
Computational Statistics and Data Analysis
Volume
53
Number
5
Pages
1873-1882
Publication identifier
http://dx.doi.org/10.1016/j.csda.2008.07.007
Metadata
Show full item record
Author(s)
Nur, Darfiana

Allingham, David

Rousseau, Judith

Mengersen, Kerrie

McVinish, Ross
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.
Subjects / Keywords
DNA sequence; hidden Markov model; Bayesian model; sensitivity analysis; α-fetoprotein; Markov chain Monte Carlo; importance sampling.

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