The Annals of Statistics

Bayesian nonparametric analysis of reversible Markov chains

Sergio Bacallado, Stefano Favaro, and Lorenzo Trippa

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We introduce a three-parameter random walk with reinforcement, called the $(\theta,\alpha,\beta)$ scheme, which generalizes the linearly edge reinforced random walk to uncountable spaces. The parameter $\beta$ smoothly tunes the $(\theta,\alpha,\beta)$ scheme between this edge reinforced random walk and the classical exchangeable two-parameter Hoppe urn scheme, while the parameters $\alpha$ and $\theta$ modulate how many states are typically visited. Resorting to de Finetti’s theorem for Markov chains, we use the $(\theta,\alpha,\beta)$ scheme to define a nonparametric prior for Bayesian analysis of reversible Markov chains. The prior is applied in Bayesian nonparametric inference for species sampling problems with data generated from a reversible Markov chain with an unknown transition kernel. As a real example, we analyze data from molecular dynamics simulations of protein folding.

Article information

Ann. Statist., Volume 41, Number 2 (2013), 870-896.

First available in Project Euclid: 29 May 2013

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62M02: Markov processes: hypothesis testing
Secondary: 62C10: Bayesian problems; characterization of Bayes procedures

Reversibility mixtures of Markov chains reinforced random walks Bayesian nonparametrics species sampling two-parameter Hoppe urn molecular dynamics


Bacallado, Sergio; Favaro, Stefano; Trippa, Lorenzo. Bayesian nonparametric analysis of reversible Markov chains. Ann. Statist. 41 (2013), no. 2, 870--896. doi:10.1214/13-AOS1102.

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Supplemental materials

  • Supplementary material: Appendices B, C and D. Appendix B describes the two-parameter HDP-HMM in relation to the $(\theta,\alpha,\beta)$ scheme. Appendix C contains all proofs from Sections 4, 5 and 6. Appendix D contains a derivation of the exact sampler mentioned in Section 7 using Coupling From the Past.