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Adaptive MCMC with Bayesian Optimization

Nimalan Mahendran‚ Ziyu Wang‚ Firas Hamze and Nando de Freitas

Abstract

This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to non-differentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex setting of sampling from constrained, discrete and densely connected probabilistic graphical models where, for each variation of the problem, one needs to adjust the parameters of the proposal mechanism automatically to ensure efficient mixing of the Markov chains.

Journal
Journal of Machine Learning Research − Proceedings Track for Artificial Intelligence and Statistics (AISTATS)
Pages
751–760
Volume
22
Year
2012