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New Machine Learning Techniques for Simulation-Based Inference InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation

K Kong, KT Matchev, S Mrenna… - arXiv preprint arXiv …, 2022 - arxiv.org
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We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio …

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BibTeX

@article{2210.01680v2,
Author        = {Kyoungchul Kong and Konstantin T. Matchev and Stephen Mrenna and Prasanth Shyamsundar},
Title         = {New Machine Learning Techniques for Simulation-Based Inference:
  InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio
  Estimation},
Eprint        = {2210.01680v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
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.},
Year          = {2022},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2210.01680v2},
File          = {2210.01680v2.pdf}
}

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