GANs for LIFE: Generative Adversarial Networks for Likelihood Free Inference
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We introduce a framework using Generative Adversarial Networks (GANs) for likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC). Our approach addresses both the key problems in likelihood--free inference, namely how to compare distributions and how to efficiently explore the parameter space. Our framework allows one to use the simulator model as a black box and leverage the power of deep networks to generate a rich set of features in a data driven fashion (as opposed to previous ad hoc approaches). Thereby it is a step towards a powerful alternative approach to LFI and ABC. On benchmark data sets, our approach improves on others with respect to scalability, ability to handle high dimensional data and complex probability distributions.
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