Statistics and machine learning for high-energy physics

These lectures introduce some of the main ideas of frequentist and Bayesian statistics as well as supervised machine learning with a focus on the probabilistic interpretation of the latter. The ideas are illustrated using simple examples from particle physics.

Publication Details

Published
2025-03-10
DOI
https://doi.org/10.23730/cyrsp-2025-002.151
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Statistics and machine learning for high-energy physics

Computational Physics and Python Applications
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Statistics and machine learning for high-energy physics

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Abstract

These lectures introduce some of the main ideas of frequentist and Bayesian statistics as well as supervised machine learning with a focus on the probabilistic interpretation of the latter. The ideas are illustrated using simple examples from particle physics.

Affordable and clean energy
Openalex Percentile: Top 77%
Computational Physics and Python Applications
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Statistics and machine learning for high-energy physics · (2025) | TGRS Research Map | TGRS