A review of ABC via density estimation, arguing that machine-learning conditional density estimators are the most promising route to scalable likelihood-free inference.
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A review of Approximate Bayesian Computation methods via density estimation: inference for simulator-models
A review of ABC via density estimation, arguing that machine-learning conditional density estimators are the most promising route to scalable likelihood-free inference.