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Adaptive MCMC with Bayesian Optimization

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 o...

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Journal:
Journal of Machine Learning Research − Proceedings Track for Artificial Intelligence and Statistics (AISTATS)
Volume:
22
Pages:
751-760
Publication date:
2012-01-01
URN:
uuid:6069aa48-4858-428f-8d02-217cacdbece8
Local pid:
cs:7211

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