Title: Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows,
Abstract: We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but
simulating data from the model is possible. SNL trains an autoregressive flow
on simulated data in order to learn a model of the likelihood in the region of
high posterior density. A sequential training procedure guides simulations and
reduces simulation cost by orders of magnitude. We show that SNL is more
robust, more accurate and requires less tuning than related neural-based
methods, and we discuss diagnostics for assessing calibration, convergence and
goodness-of-fit.