Heinrich, Lukas, Mishra-Sharma, Siddharth, Pollard, Chris and Windischhofer, Philipp (2024) Hierarchical neural simulation-based inference over event ensembles. Transactions on Machine Learning Research . ISSN 2835-8856.
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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.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QC Physics |
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics |
| Library of Congress Subject Headings (LCSH): | Data sets , Data structures (Compter science), Computer science -- Mathematics , Bayesian statistical decision theory , Machine learning |
| Journal or Publication Title: | Transactions on Machine Learning Research |
| ISSN: | 2835-8856 |
| Official Date: | 11 February 2024 |
| Dates: | Date Event 11 February 2024 Published 11 February 2024 Accepted |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Open Access (Creative Commons open licence) |
| Date of first compliant deposit: | 27 February 2024 |
| Date of first compliant Open Access: | 28 February 2024 |
| URI: | https://wrap.warwick.ac.uk/183678/ |
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