98 citations · 216 across the 13 of their papers we have counts for
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cs.LG2015★ 15 cited
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
Edward Meeds, Max 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 gener…
stat.ML2015★ 1 cited
Hamiltonian ABC
Edward Meeds, Robert Leenders, Max Welling
Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models. However, due to the difficulty in scaling likelihood…