Statistical Methods for Data Analysis in Particle Physics

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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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(Lecture Notes in Physics 909) Luca Lista-Statistical Methods for Data Analysis .pdf (4.12 MB, 需要: 5 个论坛币)

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Statistical Methods for Data Analysis in Particle Physics
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Series: Lecture Notes in Physics (Book 909)
Paperback: 172 pages
Publisher: Springer; 2016 edition (September 14, 2015)
Language: English
ISBN-10: 3319201751
ISBN-13: 978-3319201757
Shipping Weight: 1.1 pounds
http://www.amazon.com/Statistical-Methods-Analysis-Particle-Physics/dp/3319201751/ref=sr_1_1_twi_1_pap?ie=UTF8&qid=1437872674&sr=8-1&keywords=Statistical+Methods+for+Data+Analysis+in+Particle+Physics

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关键词：Statistical statistica statistic Analysis Particle particular subsequent concepts provides versus

很好的一本书，感谢分享

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

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