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Corpus ID: 216078743

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

@article{Meeds2015OptimizationMC,
title={Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference},
author={Edward Meeds and M. Welling},
journal={ArXiv},
year={2015},
volume={abs/1506.03693}
}

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. For each instantiation of u we run an optimization procedure to minimize the distance between summary statistics of the simulator and the data. After reweighing these samplesâ€¦Â CONTINUE READING