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The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, Gilles Louppe
Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for infer…
Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Joeri Hermans, Volodimir Begy, Gilles Louppe
Posterior inference with an intractable likelihood is becoming an increasingly common task in scientific domains which rely on sophisticated computer simulations. Typically, these…
Recurrent machines for likelihood-free inference
Arthur Pesah, Antoine Wehenkel, Gilles Louppe
Likelihood-free inference is concerned with the estimation of the parameters of a non-differentiable stochastic simulator that best reproduce real observations. In the absence of a…