Likelihoodfree inference with emulator networks
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Proceedings of The 1st Symposium on Advances in Approximate Bayesian Inference, PMLR 96:3253, 2019.
Abstract
Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulationbased 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. Simulations are chosen adaptively using an acquisition function which takes into account uncertainty about either the posterior distribution of interest, or the parameters of the emulator. Our approach does not rely on userdefined rejection thresholds or distance functions. We illustrate inference with emulator networks on synthetic examples and on a biophysical neuron model, and show that emulators allow accurate and efficient inference even on problems which are challenging for conventional ABC approaches.
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