Hierarchical Neural Simulation-Based Inference Over Event Ensembles

arXiv:2306.12584v2 Announce Type: replace-cross
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 …

abstract arxiv astro-ph.im cs.lg data dataset event events global hep-ex hierarchical impact inference influence parameters simulation stat.ml type work world