Statistical Inference for Generative Models with Maximum Mean Discrepancy

13 Jun 2019Francois-Xavier BriolAlessandro BarpAndrew B. DuncanMark Girolami

While likelihood-based inference and its variants provide a statistically efficient and widely applicable approach to parametric inference, their application to models involving intractable likelihoods poses challenges. In this work, we study a class of minimum distance estimators for intractable generative models, that is, statistical models for which the likelihood is intractable, but simulation is cheap... (read more)

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