- Title
- Bayesian hidden Markov model for DNA sequence segmentation: a prior sensitivity analysis
- Creator
- Nur, Darfiana; Allingham, David; Rousseau, Judith; Mengersen, Kerrie L.; McVinish, Ross
- Relation
- Computational Statistics & Data Analysis Vol. 53, Issue 5, p. 1873-1882
- Publisher Link
- http://dx.doi.org/10.1016/j.csda.2008.07.007
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2009
- Description
- 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.
- Subject
- hidden Markov model; Bayesian analysis; DNA sequences; sensitivity analysis
- Identifier
- uon:7052
- Identifier
- http://hdl.handle.net/1959.13/925050
- Identifier
- ISSN:0167-9473
- Reviewed
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