MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories

  title={MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories},
  author={Giulio Isacchini and Natanael Spisak and Armita Nourmohammad and Thierry Mora and Aleksandra M. Walczak},
  journal={Physical review. E},
  volume={105 5-2},
Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer models of the likelihood-to-evidence ratio, or equivalently the posterior function. Here we frame the inference task as an estimation of an energy function parametrized with an artificial neural network. We present an intuitive approach, named MINIMALIST, in which the optimal model of the… 

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