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Hierarchical Neural Simulation-Based Inference Over Event Ensembles

L Heinrich, S Mishra-Sharma, C Pollard… - arXiv preprint arXiv …, 2023 - arxiv.org
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… methods for simulation-based inference are designed for … augment existing simulation-based inference techniques, … In the following we explore simulation-based inference …

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BibTeX

@article{2306.12584v2,
Author        = {Lukas Heinrich and Siddharth Mishra-Sharma and Chris Pollard and Philipp Windischhofer},
Title         = {Hierarchical Neural Simulation-Based Inference Over Event Ensembles},
Eprint        = {2306.12584v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {When analyzing real-world data it is common to work with event ensembles,
which comprise sets of observations that collectively constrain the parameters
of an underlying model of interest. Such models often have a hierarchical
structure, where "local" parameters impact individual events and "global"
parameters influence the entire dataset. We introduce practical approaches for
frequentist and Bayesian dataset-wide probabilistic inference in cases where
the likelihood is intractable, but simulations can be realized via a
hierarchical forward model. We construct neural estimators for the
likelihood(-ratio) or posterior and show that explicitly accounting for the
model's hierarchical structure can lead to significantly tighter parameter
constraints. We ground our discussion using case studies from the physical
sciences, focusing on examples from particle physics and cosmology.},
Year          = {2023},
Month         = {Jun},
Url           = {http://arxiv.org/abs/2306.12584v2},
File          = {2306.12584v2.pdf}
}

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