Likelihood-free MCMC with Amortized Ratio Estimator
Simulation Based Inference Imagine we have some black-box machine; such a machine has some knobs and levels so we can change its inner configurations. The machine churns out some data for each configuration. The Simulation-based inference (SBI) solves the inverse problem that is given some data, estimating the configuration (Frequentist approach) or sampling the configuration from the posterior distribution (for Bayesian approach). For a formal definition and review of current methods for SBI, see this paper....