Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

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Authors Iain Murray, David C. Sterratt, George Papamakarios
Journal/Conference Name AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics
Paper Category
Paper 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.
Date of publication 2018
Code Programming Language Multiple

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