Binary classification based Monte Carlo simulation
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Abstract (arXiv)
Acceptance-rejection (AR), Independent Metropolis Hastings (IMH) or importance sampling (IS) Monte Carlo (MC) simulation algorithms all involve computing ratios of probability density functions (pdfs). On the other hand, classifiers discriminate labeled samples produced by a mixture of two distributions and can be used for approximating the ratio of the two corresponding pdfs.This bridge between simulation and classification enables us to propose pdf-free versions of pdf-ratio-based simulation algorithms, where the ratio is replaced by a surrogate function computed via a classifier. From a probabilistic modeling perspective, our procedure involves a structured energy based model which can easily be trained and is compatible with the classical samplers.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 39 references that are in the map. First posted on arXiv 29 Jul 2023.
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References · 11
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2022Generative Adversarial Networkscited by 5,603
- 2018MINE: Mutual Information Neural Estimationcited by 680
- 2017Generative Adversarial Networks: An Overviewcited by 588
- 2016Deep Structured Energy Based Models for Anomaly Detectioncited by 113
- 2020On Contrastive Learning for Likelihood-free Inferencecited by 96
- 2016Likelihood-free inference by ratio estimationcited by 80
- 2017Continuous-Time Flows for Efficient Inference and Density Estimationcited by 21
- 2017Denoising Adversarial Autoencoderscited by 20
- 2020Variational Inference with Continuously-Indexed Normalizing Flowscited by 14
- 2021A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABCcited by 8
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