Abstract
Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class of methods for this likelihood-free problem uses a classifier to distinguish between pairs of parameter-observation samples generated using the simulator and pairs sampled from some reference distribution, which implicitly learns a density ratio proportional to the likelihood. Another popular class of methods fits a conditional distribution to the parameter posterior directly, and a particular recent variant allows for the use of flexible neural density estimators for this task. In this work, we show that both of these approaches can be unified under a general contrastive learning scheme, and clarify how they should be run and compared.
Original language | English |
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Title of host publication | Proceedings of the 37th International Conference on Machine Learning |
Publisher | PMLR |
Pages | 2771-2781 |
Number of pages | 15 |
Publication status | Published - 18 Jul 2020 |
Event | Thirty-seventh International Conference on Machine Learning 2020 - Virtual conference Duration: 13 Jul 2020 → 18 Jul 2020 https://icml.cc/ |
Publication series
Name | Proceedings of Machine Learning Research |
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Publisher | PMLR |
Volume | 119 |
ISSN (Electronic) | 2640-3498 |
Conference
Conference | Thirty-seventh International Conference on Machine Learning 2020 |
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Abbreviated title | ICML 2020 |
City | Virtual conference |
Period | 13/07/20 → 18/07/20 |
Internet address |