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Bibliographic Details
Main Author: Fowlie, Andrew
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2503.22188
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author Fowlie, Andrew
author_facet Fowlie, Andrew
contents In collider physics, experiments are often based on counting the numbers of events in bins of a histogram. We present a new way to build and analyze statistical models that describe these experiments, based on the probabilistic programming language Stan and the HistFactory specification. A command-line tool transpiles HistFactory models into Stan code and data files. Because Stan is an imperative language, it enables richer and more detailed modeling, as modeling choices defined in the HistFactory declarative specification can be tweaked and adapted in the Stan language. Stan was constructed with automatic differentiation, allowing modern computational algorithms for sampling and optimization.
format Preprint
id arxiv_https___arxiv_org_abs_2503_22188
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle stanhf: HistFactory models in the probabilistic programming language Stan
Fowlie, Andrew
High Energy Physics - Phenomenology
High Energy Physics - Experiment
Data Analysis, Statistics and Probability
In collider physics, experiments are often based on counting the numbers of events in bins of a histogram. We present a new way to build and analyze statistical models that describe these experiments, based on the probabilistic programming language Stan and the HistFactory specification. A command-line tool transpiles HistFactory models into Stan code and data files. Because Stan is an imperative language, it enables richer and more detailed modeling, as modeling choices defined in the HistFactory declarative specification can be tweaked and adapted in the Stan language. Stan was constructed with automatic differentiation, allowing modern computational algorithms for sampling and optimization.
title stanhf: HistFactory models in the probabilistic programming language Stan
topic High Energy Physics - Phenomenology
High Energy Physics - Experiment
Data Analysis, Statistics and Probability
url https://arxiv.org/abs/2503.22188