Stochastic adaptive selection of weights in the simulated tempering algorithm


DOI: 10.1007/BF03178920

Cite this article as:
Ramponi, A. J. Ital. Statist. Soc. (1998) 7: 27. doi:10.1007/BF03178920


Simulated Tempering is a new MCMC scheme that has been recently introduced to speed up the convergence of slow Markov chains. The implementation of the procedure depends on the choice of a set of parameters, the weights, which affect the efficiency of the sampling algorithm. In this paper we prove the a.s. convergence of a stochastic algorithm driven by a non-homogeneous Markov chain which select the weights adaptively. The problem of estimating the nonnalizing constants of a family of unnonnalized densitieshk, k=1, …,M is also discussed and an example of application is reported.


Markov Chain Monte Carlo Simulated Tempering Stochastic Algorithms Nonnalizing Constants 

Copyright information

© Società Italiana di Statistica 1998

Authors and Affiliations

  1. 1.Department of Pure and Applied MathematicsUniversity of L’AquilaL’AquilaItaly

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