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Author Narsky, Ilya, author

Title Statistical analysis techniques in particle physics : fits, density estimation and supervised learning / Ilya Narsky and Frank C. Porter
Published Weinheim : Wiley-VCH, 2013
Online access available from:
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Description 1 online resource (xvii, 441 pages) : illustrations
Contents Why We Wrote This Book and How You Should Read It -- Parametric Likelihood Fits -- Goodness of Fit -- Resampling Techniques -- Density Estimation -- Basic Concepts and Definitions of Machine Learning -- Data Preprocessing -- Linear Transformations and Dimensionality Reduction -- Introduction to Classification -- Assessing Classifier Performance -- Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression -- Neural Networks -- Local Learning and Kernel Expansion -- Decision Trees -- Ensemble Learning -- Reducing Multiclass to Binary -- How to Choose the Right Classifier for Your Analysis and Apply It Correctly -- Methods for Variable Ranking and Selection -- Bump Hunting in Multivariate Data -- Software Packages for Machine Learning -- Appendix A: Optimization Algorithms
Summary Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students
Notes Includes index
Bibliography Includes bibliographical references and index
Notes Print version record
Subject Condensed matter.
Particles (Nuclear physics) -- Statistical methods.
Form Electronic book
Author Porter, Frank Clifford, author
ISBN 1306028868 (MyiLibrary)
3527410864 (Paper)
3527677291 (electronic bk.)
3527677305 (electronic bk.)
3527677313 (electronic bk.)
3527677321 (electronic bk.)
9781306028868 (MyiLibrary)
9783527410866 (Paper)
9783527677290 (electronic bk.)
9783527677306 (electronic bk.)
9783527677313 (electronic bk.)
9783527677320 (electronic bk.)