Statistical methods for data analysis in particle physics / Luca Lista.

By: Lista, Luca [author.]
Material type: TextTextSeries: Lecture notes in physics: volume 909.Publisher: Cham : Springer, [2015]Copyright date: ©2016Description: 1 online resource (xix, 172 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9783319201764; 331920176XSubject(s): Nuclear physics -- Statistical methods | Mensuration & systems of measurement | Probability & statistics | Quantum physics (quantum mechanics & quantum field theory) | Science -- Weights & Measures | Mathematics -- Probability & Statistics -- General | Science -- Nuclear Physics | Nuclear physics -- Statistical methods | kwantumtheorie | quantum theory | deeltjes | particles | toegepaste statistiek | applied statistics | meting | measurement | meettechnieken | measurement techniques | fysica | physics | Physics (General) | Fysica (algemeen)Genre/Form: Electronic books. DDC classification: 539.7/20727 LOC classification: QC793.47.S83Online resources: Click here to access online
Contents:
Probability theory -- Probability Distribution Functions -- Bayesian approach to probability -- Random numbers and Monte Carlo Methods -- Parameter estimate -- Confidence intervals -- Hypothesis tests -- Upper Limits.
Abstract: This concise set of course-based notes provides the reader with the main concepts and tools to perform statistical analysis of experimental data, in particular in the field of high-energy physics (HEP). First, an introduction to probability theory and basic statistics is given, mainly as reminder from advanced undergraduate studies, yet also in view to clearly distinguish the Frequentist versus Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on upper limits as many applications in HEP concern hypothesis testing, where often the main goal is to provide better and better limits so as to be able to distinguish eventually between competing hypotheses or to rule out some of them altogether. Many worked examples will help newcomers to the field and graduate students to understand the pitfalls in applying theoretical concepts to actual data.
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Probability theory -- Probability Distribution Functions -- Bayesian approach to probability -- Random numbers and Monte Carlo Methods -- Parameter estimate -- Confidence intervals -- Hypothesis tests -- Upper Limits.

Includes bibliographical references.

Online resource; title from PDF title page (SpringerLink, viewed July 30, 2015).

This concise set of course-based notes provides the reader with the main concepts and tools to perform statistical analysis of experimental data, in particular in the field of high-energy physics (HEP). First, an introduction to probability theory and basic statistics is given, mainly as reminder from advanced undergraduate studies, yet also in view to clearly distinguish the Frequentist versus Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on upper limits as many applications in HEP concern hypothesis testing, where often the main goal is to provide better and better limits so as to be able to distinguish eventually between competing hypotheses or to rule out some of them altogether. Many worked examples will help newcomers to the field and graduate students to understand the pitfalls in applying theoretical concepts to actual data.

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