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Neural likelihood-free inference

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If you have a question about this talk, please contact Isaac Reid .

Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.

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: https://arxiv.org/abs/1605.06376, NeurIPS 2016

[2] Neural likelihood estimate: https://arxiv.org/abs/1805.07226, AISTATS 2019

[3] Neural sufficient statistics: https://arxiv.org/abs/2010.10079, ICLR 2021

[4] Review: https://www.pnas.org/doi/10.1073/pnas.1912789117, PNAS 2020

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

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