Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis

Darfiana Nur, David Allingham, Judith Rousseau, Kerrie L. Mengersen and Ross McVinish

Computational Statistics & Data Analysis, 2009, vol. 53, issue 5, 1873-1882

Abstract: The sensitivity to the specification of the prior in a hidden Markov model describing homogeneous segments of DNA sequences is considered. An intron from the chimpanzee [alpha]-fetoprotein gene, which plays an important 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 illustrate 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.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:5:p:1873-1882

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