Efficient simulated annealing algorithms for Bayesian parameter estimation

Christophe Andrieu; Arnaud Doucet

doi:10.1117/12.323808

22 September 1998 Efficient simulated annealing algorithms for Bayesian parameter estimation

Christophe Andrieu, Arnaud Doucet

Proceedings Volume 3459, Bayesian Inference for Inverse Problems; (1998) https://doi.org/10.1117/12.323808
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

In this paper we present simple conditions related to geometric ergodicity of Markov chains which ensure the convergence in a given sense of the simulated annealing algorithm. We prove that convergence of the algorithm occurs for a proper sequence of temperatures when a local minorization condition of the transition kernels and a drift condition are satisfied. This result may be useful in a Bayesian framework, where it is possible to take advantage of the statistical structure of the problem in order to perform efficient optimization. This is illustrated on several examples.

Citation Download Citation

Christophe Andrieu and Arnaud Doucet "Efficient simulated annealing algorithms for Bayesian parameter estimation", Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); https://doi.org/10.1117/12.323808

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