2009 BayesianHiddenMarkovModelforDNA
- (Nur et al., 2009) ⇒ Darfiana Nur, David Allingham, Judith Rousseau, Kerrie L Mengersen, and Ross McVinish. (2009). “Bayesian Hidden Markov Model for DNA Sequence Segmentation: A Prior Sensitivity Analysis.” In: Computational Statistics & Data Analysis, 53(5). doi:dx.doi.org/10.1016/j.csda.2008.07.007
Subject Headings: DNA Sequence Segmentation
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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.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2009 BayesianHiddenMarkovModelforDNA | Darfiana Nur David Allingham Judith Rousseau Kerrie L Mengersen Ross McVinish | Bayesian Hidden Markov Model for DNA Sequence Segmentation: A Prior Sensitivity Analysis | dx.doi.org/10.1016/j.csda.2008.07.007 | 2009 |