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 normalizing constants of a family of unnormalized densities k = I, ... , M is also discussed and an example of application is reported.
Ramponi, A. (1998). Stochastic adaptive selection of weights in the simulated tempering algorithm. JOURNAL OF THE ITALIAN STATISTICAL SOCIETY, 7(1), 27-75.
Stochastic adaptive selection of weights in the simulated tempering algorithm
RAMPONI, ALESSANDRO
1998-01-01
Abstract
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 normalizing constants of a family of unnormalized densities k = I, ... , M is also discussed and an example of application is reported.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.