Likelihood-free inference with emulator networks

23 May 2018Jan-Matthis LueckmannGiacomo BassettoTheofanis KaraletsosJakob H. Macke

Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data -- both local emulators which approximate the likelihood for specific observed data, as well as global ones which are applicable to a range of data... (read more)

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