Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/925050
- Title
- Bayesian hidden Markov model for DNA sequence segmentation: a prior sensitivity analysis
- Author/Creator
-
Nur, Darfiana;
Allingham, David;
Rousseau, Judith;
Mengersen, Kerrie L.;
McVinish, Ross
- Institution
- The University of Newcastle. Faculty of Science & Information Technology, School of Mathematical and Physical Sciences
- 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.
- 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
- Date
- 2009
- Publisher
- Elsevier
- Keyword(s)
-
hidden Markov model;
Bayesian analysis;
DNA sequences;
sensitivity analysis
- Resource Type
- journal article
- Identifier
- http://hdl.handle.net/1959.13/925050
- Identifier
- ISSN:0167-9473
- Reviewed
55 Visitors
77 Hits
4 Downloads
List Of Titles
