Variational Bayesian Monte Carlo

Luigi Acerbi

Neural Information Processing Systems 

We introduce here a novel sample-efficient inference framework, V ariational Bayesian Monte Carlo (VBMC). VBMC combines variational inference with Gaussian-process based, active-sampling Bayesian quadrature, using the latter to efficiently approximate the intractable integral in the variational objective.

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