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

Computational Statistics and Data Analysis

Volumn 53, Issue 5, 2009, Pages 1873-1882

  Nur, Darfiana ^a   Allingham, David ^a   Rousseau, Judith ^b   Mengersen, Kerrie L ^c   McVinish, Ross ^c  

^a UNIVERSITY OF NEWCASTLE   (Australia)

^b UNIVERSITÉ PARIS DAUPHINE   (France)

^c QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DNA; DNA SEQUENCES; IMPORTANCE SAMPLING; MIXTURES; SENSITIVITY ANALYSIS; SPECIFICATIONS; TRELLIS CODES;

ALPHA FETOPROTEIN; BAYESIAN; DIRICHLET PRIOR; EMBRYONIC DEVELOPMENT; HYPERPARAMETERS; PRIOR DISTRIBUTION;

HIDDEN MARKOV MODELS;

EID: 60349084315     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2008.07.007     Document Type: Article

References (17)

1. Besag J., Green P., Higdon D., and Mengersen K. Bayesian computation and stochastic systems. Statist. Sci. 10 (1995) 3-66
2. Boys R.J., and Henderson D.A. A Bayesian approach to DNA sequence segmentation. Biometrics 60 (2004) 573-588
3. Boys R.J., Henderson D.A., and Wilkinson D.J. Detecting homogeneous segments in DNA sequences by using Hidden Markov models. Appl. Stat. 49 (2000) 269-285
4. Braun J.V., and Müller H.-G. Statistical methods for DNA sequence segmentation. Statist. Sci. 13 (1998) 142-162
5. Brown M.P., Hughey R., Krogh A., Mian I.S., Sjölander K., and Haussler D. Using Dirichlet mixture priors to derive hidden Markov models for protein families. In: Hunter L., Searls D., and Shavlik J. (Eds). Proceedings of the First International Conference on Intelligent Systems for Molecular Biology (ISMB-93) (1993), AAAI/MIT Press, Menlo Park, CA 47-55
6. Churchill G.A. Stochastic models for heterogeneous DNA sequences. B. Math. Biol. 51 (1989) 79-94
7. De Fonzo V., Aluffi-Pentini F., and Parisi V. Hidden Markov models in bioinformatics. Curr. Bioinformatics 2 (2007) 49-61
8. Durbin R., Eddy S.R., Krogh A., and Mitchison G. Biological Sequence Analysis (1998), Cambridge University Press, Cambridge
9. Gelman A., Carlin J.B., Stern H.S., and Rubin D.B. Bayesian Data Analysis (1995), Chapman and Hall, London
10. Geyer C. Practical Markov chain Monte Carlo. Statistical Science 7 (1992) 473-483 (With discussion)
11. Liu J.S., and Logvinenko T. Bayesian methods in biological sequence analysis. In: Balding D.J., Bishop M., and Cannings C. (Eds). Handbook of Statistical Genetics. 2nd edition (2003), John Wiley & Sons, Chichester 66-93
12. Lunn D.J., Thomas A., Best N., and Spiegelhalter D. WinBUGS-A Bayesian modelling framework: Concepts, structure, and extensibility. Stat. Comput. 10 (2000) 325-337
13. MacDonald I.L., and Zucchini W. Hidden Markov and Other Models for Discrete-Valued Time Series (1997), Chapman and Hall, London
14. McVinish, R., Mengersen, K.L., Nur, D., Rousseau, J., Guihenneuc-Jouyaux, C., Use of Importance sampling for repeated MCMC. School of Mathematical Sciences, Queensland University of Technology, 2008
15. Mengersen K.L., Robert C.P., and Guihenneuc-Jouyaux C. MCMC convergence diagnostics: A review. In: Berger J., Bernardo J., Dawid A.P., and Smith A.F.M. (Eds). Bayesian Statistics 6 (1999), Oxford Sciences Publications 415-440
16. Meyn S.P., and Tweedie R.L. Markov Chains and Stochastic Stability (1993), Springer-Verlag, London
17. Nishio H., Gibbs P.E.M., Minghetti P.P., Zielinski R., and Dugaiczyk A. The chimpanzee α-fetoprotein-encoding gene shows structural similarity to that of gorilla but distinct differences from that of human. Gene 162 (1995) 213-220