Mining gold from implicit models to improve likelihood-free inference

[en] Simulators often provide the best description of real-world phenomena; however, they also lead to challenging inverse problems because the density they implicitly define is often intractable. We present a new suite of simulation-based inference techniques that go beyond the traditional Approximate Bayesian Computation approach, which struggles in a high-dimensional setting, and extend methods that use surrogate models based on neural networks. We show that additional information, such as the joint likelihood ratio and the joint score, can often be extracted from simulators and used to augment the training data for these surrogate models. Finally, we demonstrate that these new techniques are more sample efficient and provide higher-fidelity inference than traditional methods.

Disciplines :

Computer science
Physics

Author, co-author :

Brehmer, Johann

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

Pavez, Juan

Cranmer, Kyle

Language :

English

Title :

Mining gold from implicit models to improve likelihood-free inference

Publication date :

20 February 2020

Journal title :

Proceedings of the National Academy of Sciences of the United States of America

ISSN :

0027-8424

eISSN :

1091-6490

Publisher :

National Academy of Sciences, Washington DC, United States - District of Columbia

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://arxiv.org/abs/1805.12244

Commentary :

Code available at https://github.com/johannbrehmer/simulator-mining-example

Available on ORBi :

since 28 June 2018

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Bibliography

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