Bayesian model learning based on a parallel MCMC strategy (original) (raw)
Abstract
We introduce a novel Markov chain Monte Carlo algorithm for estimation of posterior probabilities over discrete model spaces. Our learning approach is applicable to families of models for which the marginal likelihood can be analytically calculated, either exactly or approximately, given any fixed structure. It is argued that for certain model neighborhood structures, the ordinary reversible Metropolis-Hastings algorithm does not yield an appropriate solution to the estimation problem. Therefore, we develop an alternative, non-reversible algorithm which can avoid the scaling effect of the neighborhood. To efficiently explore a model space, a finite number of interacting parallel stochastic processes is utilized. Our interaction scheme enables exploration of several local neighborhoods of a model space simultaneously, while it prevents the absorption of any particular process to a relatively inferior state. We illustrate the advantages of our method by an application to a classification model. In particular, we use an extensive bacterial database and compare our results with results obtained by different methods for the same data.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime Subscribe now
Buy Now
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Instant access to the full article PDF.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.
References
- Altekar G., Dwarkadas S., Huelsenbeck J.P., and Ronquist F. 2004. Parallel metropolis-coupled Markov chain Monte Carlo for Bayesian phylogenetic inference. Bioinformatics 20: 407–415.
Article Google Scholar - Brooks S.P., Giudici P., and Roberts G.O. 2003. Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions. J. Roy. Statist. Soc. B 65: 3–39.
Article MATH MathSciNet Google Scholar - Carlin B.P. and Chib S. 1995. Bayesian model choice via Markov-chain Monte Carlo methods. J. Roy. Statist. Soc. B 57: 473–484.
MATH Google Scholar - Chib S. and Greenberg E. 1995. Understanding the Metropolis-Hastings algorithm. Amer. Statist. 49: 327–335.
Article Google Scholar - Corander J., Gyllenberg M., and Koski T. 2006. Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy. Submitted to J. Statist. Comput. Simulation.
- Corander J., Waldmann P., Marttinen P., and Sillanpää M.J. 2004. BAPS 2: enhanced possibilities for the analysis of genetic population structure. Bioinformatics 20: 2363–2369.
Article Google Scholar - Diaconis P., Holmes S., and Neal R.M. 2000. Analysis of a nonreversible Markov chain sampler. Ann. App. Prob. 10: 726–752.
Article MATH MathSciNet Google Scholar - Doucet A., Godsill S., and Andrieu C. 2000. On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. Comput. 10: 197–208.
Article Google Scholar - Farmer J.J., Davis B.R., and Hickmanbrenner F.W. 1985. Biochemical identification of new species and biogroups of Enterobacteriaceae isolated from clinical specimens. J. Clin. Microbiology 21: 46–76.
Google Scholar - Geyer C.J. and Thompson E.A. 1995. Annealing Markov chain Monte Carlo with applications to ancestral inference. J. Amer. Stat. Assoc. 90: 909–920.
Article MATH Google Scholar - Gidas B. 1985. Nonstationary Markov chains and convergence of the annealing algorithm. J. Statist. Phys. 39: 73–130.
Article MATH MathSciNet Google Scholar - Giudici P. and Castelo R. 2003. Improving Markov chain Monte Carlo search for data mining. Machine Learning 50: 127–158.
Article MATH Google Scholar - Green P. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika82: 711–732.
Article MATH MathSciNet Google Scholar - Gyllenberg H.G., Gyllenberg M., Koski T., Lund T., Schindler J., and Verlaan, M. 1997. Classification of Enterobacteriaceae by minimization of stochastic complexity. Microbiology 143: 721–732.
Article Google Scholar - Gyllenberg H.G., Gyllenberg M., Koski T., and Lund T. 1998. Stochastic complexity as a taxonomic tool. Computer Methods and Programs in Biomedicine 56: 11–22.
Article Google Scholar - Gyllenberg H.G., Gyllenberg M., Koski T., Lund T., and Schindler J. 1999a. An assessment of cumulative classification. Quantitative Microbiology 1: 7–27.
Article Google Scholar - Gyllenberg H.G., Gyllenberg M., Koski T., Lund T., and Schindler J. 1999b. Enterobacteriaceae taxonomy approached by minimization of stochastic complexity. Quantitative Microbiology 1: 157–170.
Article Google Scholar - Gyllenberg H.G., Gyllenberg M., Koski T., Lund T., Mannila H., and Meek C. 1999c. Singling out ill-fit items in a classification. Application to the taxonomy of Enterobacteriaceae. Archives of Control Sciences 9: 97–105.
MathSciNet MATH Google Scholar - Gyllenberg M., Koski T., Lund T., and Gyllenberg H.G. 1999. Bayesian predictive identification and cumulative classification of bacteria. Bulletin of Mathematical Biology 61: 85–111.
Article Google Scholar - Häggström O. 2002. Finite Markov Chains and Algorithmic Applications. Cambridge, Cambridge University Press.
MATH Google Scholar - Isaacson D.L. and Madsen R.W. 1976. Markov Chains: Theory and Applications, New York, Wiley.
- Jensen S.T., Liu S., Zhou Q., and Liu J.S. 2004. Computational discovery of gene regulatory binding motifs: A Bayesian perspective. Stat. Sci. 19: 188–204.
Article MATH MathSciNet Google Scholar - Laskey K.B. and Myers J.W. 2003. Population Markov chain Monte Carlo. Machine Learning 50: 175–196.
Article MATH Google Scholar - Robert C.P. and Casella G. 2005. Monte Carlo Statistical Methods. 2nd edition, New York, Springer.
MATH Google Scholar - Schervish M.J. 1995. Theory of Statistics. New York, Springer.
MATH Google Scholar - Sisson S.A. 2005. Transdimensional Markov chains: A decade of progress and future perspectives. J. Amer. Stat. Assoc. 100: 1077–1089.
Article MathSciNet MATH Google Scholar - Tierney L.M. 1994. Markov chains for exploring posterior distributions. Ann. Statist. 22: 1701–1728.
MATH MathSciNet Google Scholar
Author information
Authors and Affiliations
- Rolf Nevanlinna Institute, Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68, FIN-00014, Finland
Jukka Corander, Mats Gyllenberg & Timo Koski - Department of Mathematics, University of Linköping, S-58183, Linköping, Sweden
Timo Koski
Authors
- Jukka Corander
You can also search for this author inPubMed Google Scholar - Mats Gyllenberg
You can also search for this author inPubMed Google Scholar - Timo Koski
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toJukka Corander.
Rights and permissions
About this article
Cite this article
Corander, J., Gyllenberg, M. & Koski, T. Bayesian model learning based on a parallel MCMC strategy.Stat Comput 16, 355–362 (2006). https://doi.org/10.1007/s11222-006-9391-y
- Received: 01 July 2005
- Accepted: 01 June 2006
- Issue Date: December 2006
- DOI: https://doi.org/10.1007/s11222-006-9391-y