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Inference as Learning

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If you have a question about this talk, please contact Zoubin Ghahramani.

How can we do Bayesian inference if the likelihood is not available? This situation arises in simulator-based models, where the model can be easily simulated but its likelihood is intractable. One can learn to perform inference in such models based only on simulation data, by casting inference as a learning problem. In this talk, I will describe a strategy for doing this efficiently using Bayesian conditional density estimation, and compare it with established likelihood-free inference techniques such as Approximate Bayesian Computation.

This talk is part of the Machine Learning @ CUED series.

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