Likelihood-free inference via classification

18 Jul 2014Michael U. GutmannRitabrata DuttaSamuel KaskiJukka Corander

Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data... (read more)

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