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

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

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

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 α-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.

Original languageEnglish
Pages (from-to)1873-1882
Number of pages10
JournalComputational Statistics and Data Analysis
Volume53
Issue number5
DOIs
Publication statusPublished - 15 Mar 2009
Externally publishedYes

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