Statistical methods for data analysis in particle physics
- 作者： Lista, Luca, author.
- 其他作者：
- 其他題名：
- Lecture notes in physics ;
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Lecture notes in physics,volume 909
- 主題： Nuclear physics--Statistical methods. , Physics. , Elementary Particles, Quantum Field Theory. , Measurement Science and Instrumentation. , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
- ISBN： 9783319201764 (electronic bk.) 、 9783319201757 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Preface -- Probability theory -- Probability Distribution Functions -- Bayesian approach to probability -- Random numbers and Monte Carlo Methods -- Parameter estimate -- Confidence intervals -- Hypothesis tests -- Upper Limits -- Bibliography.
- 摘要註: 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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讀者標籤：
- 系統號： 005357234 | 機讀編目格式
館藏資訊
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.