Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

NeurIPS 2015 Edward MeedsMax Welling

We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a vector of random numbers u, in such a way that the outcome, knowing u, is deterministic... (read more)

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