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Neural likelihood-free inference

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Likelihood-free inference (LFI) is a technique for Bayesian inference in implicit statistical models. Such models have wide application in science and engineering, from inferring the R-value of an epidemic, to analyzing a stochastic volatility model. In this reading group, we will provide an introductory overview on LFI , covering methods, use cases and challenges. Specifically, we will focus on recent neural network-based methods. No preliminary knowledge is assumed.

No reading is required, but the following materials may be useful:

[1] Neural posterior estimate:, NeurIPS 2016

[2] Neural likelihood estimate:, AISTATS 2019

[3] Neural sufficient statistics:, ICLR 2021

[4] Review:, PNAS 2020

This talk is part of the Machine Learning Reading Group @ CUED series.

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