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Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers

Abstract:

In this paper we address the widely-experienced difficulty in tuning Hamiltonian-based Monte Carlo samplers. We develop an algorithm that allows for the adaptation of Hamiltonian and Riemann manifold Hamiltonian Monte Carlo samplers using Bayesian optimization that allows for infinite adaptation of the parameters of these samplers. We show that the resulting sampling algorithms are ergodic, and demonstrate on several models and data sets that the use of our adaptive algorithms makes it is eas...

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Host title:
International Conference on Machine Learning (ICML)
Publication date:
2013-01-01
UUID:
uuid:0b441faa-a239-4eb6-a9e1-d3acdd420807
Local pid:
cs:7205
Deposit date:
2015-03-31

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