Work

Hierarchical Neural Simulation-Based Inference Over Event Ensembles

Year: 2023

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2306.12584

Authors: Heinrich, Lukas, Mishra-Sharma, Siddharth, Pollard, Chris, Windischhofer, Philipp

Institutions: Technical University of Munich, Harvard University, The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Massachusetts Institute of Technology, University of Warwick, University of Chicago

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Energy, University of Chicago, Aspen Center for Physics, Deutsche Forschungsgemeinschaft, Office of Science, Science and Technology Facilities Council, High Energy Physics

Awards: G130463927, G1418946953, G1897041155, G2229954460, G2366492356, G2432923657, THEORETICAL HIGH ENERGY PHYSICS, AI Institute: AI Research Institute for Fundamental Interactions, G4066435496, EXC 2094: ORIGINS: Vom Ursprung des Universums bis zu den ersten Bausteinen des Lebens, THEORETICAL HIGH ENERGY PHYSICS, G6794394390, Programme of Research in Experimental Particle Physics at the University of Warwick: 2022-2025, Programs on Critical Problems in Physics, Astrophysics and Biophysics at the Aspen Center for Physics, G97843138

OpenAlex ID: W4381826880

Cites:

Cited by: 6

Related to: 10