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New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation
by Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar
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Submission summary
| Authors (as registered SciPost users): | Kyoungchul Kong · Konstantin Matchev · Stephen Mrenna · Prasanth Shyamsundar |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2210.01680v2 (pdf) |
| Code repository: | https://gitlab.com/prasanthcakewalk/code-and-data-availability/-/tree/master/arXiv_2210.01680 |
| Date accepted: | 2023-04-11 |
| Date submitted: | 2023-02-14 05:22 |
| Submitted by: | Shyamsundar, Prasanth |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
Abstract
We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.
List of changes
We have added a citation as suggested by the referee. We have also fixed a few typos and made minor updates to the text to improve readability.
Published as SciPost Phys. Codebases 14 (2023) , SciPost Phys. Codebases 14-r0.1 (2023)