Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Hermans, Joeri; Begy, Volodimir; Louppe, Gilles

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Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Hermans, Joeri; Begy, Volodimir; Louppe, Gilles

2020 • In Proceedings of the 37th International Conference on Machine Learning

Peer reviewed

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https://hdl.handle.net/2268/235737

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Abstract :

[en] Posterior inference with an intractable likelihood is becoming an increasingly common task in scientific domains which rely on sophisticated computer simulations. Typically, these forward models do not admit tractable densities forcing practitioners to rely on approximations. This work introduces a novel approach to address the intractability of the likelihood and the marginal model. We achieve this by learning a flexible amortized estimator which approximates the likelihood-to-evidence ratio. We demonstrate that the learned ratio estimator can be embedded in \textsc{mcmc} samplers to approximate likelihood-ratios between consecutive states in the Markov chain, allowing us to draw samples from the intractable posterior. Techniques are presented to improve the numerical stability and to measure the quality of an approximation. The accuracy of our approach is demonstrated on a variety of benchmarks against well-established techniques. Scientific applications in physics show its applicability.

Disciplines :

Computer science

Author, co-author :

Hermans, Joeri ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data

Begy, Volodimir

Louppe, Gilles ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data

Language :

English

Title :

Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Publication date :

July 2020

Event name :

37th International Conference on Machine Learning

Event date :

July 13-18, 2020

Audience :

International

Main work title :

Proceedings of the 37th International Conference on Machine Learning

Pages :

4239-4248

Peer reviewed :

Peer reviewed

Additional URL :

https://arxiv.org/abs/1903.04057

Available on ORBi :

since 22 May 2019

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