, Volume 7, Issue 1, pp 27-55

Stochastic adaptive selection of weights in the simulated tempering algorithm

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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 densitiesh k , k=1, …,M is also discussed and an example of application is reported.