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171103 ||| eng |
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|a 9783319628400
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|a Lista, Luca
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|a Statistical Methods for Data Analysis in Particle Physics
|h Elektronische Ressource
|c by Luca Lista
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| 250 |
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|a 2nd ed. 2017
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2017, 2017
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| 300 |
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|a XVI, 257 p. 101 illus., 97 illus. in color
|b online resource
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| 505 |
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|a Preface -- Probability theory -- Probability Distribution Functions -- Bayesian Approach to Probability -- Random Numbers and Monte Carlo Methods -- Parameter Estimate -- Combining Measurements -- Confidence Intervals -- Convolution and Unfolding -- Hypothesis Tests -- Discoveries and Upper Limits -- Index
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| 653 |
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|a Measurement
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| 653 |
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|a Quantum field theory
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| 653 |
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|a Statistics
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| 653 |
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|a Elementary particles (Physics)
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| 653 |
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|a Elementary Particles, Quantum Field Theory
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| 653 |
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|a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Measuring instruments
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| 653 |
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|a Measurement Science and Instrumentation
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Lecture Notes in Physics
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| 028 |
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|a 10.1007/978-3-319-62840-0
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| 856 |
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|u https://doi.org/10.1007/978-3-319-62840-0?nosfx=y
|x Verlag
|3 Volltext
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|a 530.14
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| 520 |
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|a This concise set of course-based notes provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP). First, the book provides an introduction to probability theory and basic statistics, mainly intended as a refresher from readers’ advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on both discoveries and upper limits, as many applications in HEP concern hypothesis testing, where the main goal is often to provide better and better limits so as to eventually be able to distinguish between competing hypotheses, or to rule out some of them altogether. Many worked-out examples will help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data. This new second edition significantly expands on the original material, with more background content (e.g. the Markov Chain Monte Carlo method, best linear unbiased estimator), applications (unfolding and regularization procedures, control regions and simultaneous fits, machine learning concepts) and examples (e.g. look-elsewhere effect calculation).
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