Statistical Analysis Techniques in Particle Physics: Fits, Density Estimation and Supervised Learning - Softcover

Narsky, Ilya; Porter, Frank C.

Softcover

ISBN 10: 3527410864 ISBN 13: 9783527410866

Verlag: Wiley-VCH, 2013

Inhaltsangabe

The first book written specifically with physicists in mind on analysis techniques in particle physics with an emphasis on machine learning techniques.
Based on lectures given by the authors at Stanford and Caltech, this practical approach shows by means of analysis examples how observables are extracted from data, how signal and background are estimated, and how accurate error estimates are obtained exploiting uni- and multivariate analysis techniques, such as non-parametric density estimation, likelihood fits, neural networks, support vector machines, decision trees, and ensembles of classifiers. It includes simple code snippets that run on popular software suites such as Root and Matlab, and either include the codes for generating data or make use of publically available data that can be downloaded from the Web.
Primarily aimed at master and very advanced undergraduate students, this text is also intended for study and research.

Über diesen Titel

Über die Autorin bzw. den Autor

The authors are experts in the use of statistics in particle physics data analysis. Frank C. Porter is Professor at Physics at the California Institute of Technology and has lectured extensively at CalTech, the SLAC Laboratory at Stanford, and elsewhere. Ilya Narsky is Senior Matlab Developer at The MathWorks, a leading developer of technical computing software for engineers and scientists, and the initiator of the StatPatternRecognition, a C++ package for statistical analysis of HEP data. Together, they have taught courses for graduate students and postdocs.

Von der hinteren Coverseite

Based on lectures given by the authors at Stanford and Caltech, this practical approach shows by means of analysis examples how observables are extracted from data, how signal and background are estimated, and how accurate error estimates are obtained exploiting uni- and multivariate analysis techniques. The book includes simple code snippets that run on the popular software suite MATLAB. These snippets make use of publicly available datasets that can be downloaded from the Web.

Primarily aimed at PhD and very advanced undergraduate students, this text can be also used by researchers.

From the contents:

• Parametric likelihood fits
• Goodness of fit
• Resampling techniques
• Density estimation
• Data pre-processing
• Linear transformations and dimensionality reduction
• Introduction to classifi cation
• Assessing classifi er performance
• Linear classification
• Neural networks
• Local learning and kernel expansion
• Decision trees
• Ensemble learning
• Reducing multiclass to binary
• Methods for variable ranking and selection

Aus dem Klappentext

Based on lectures given by the authors at Stanford and Caltech, this practical approach shows by means of analysis examples how observables are extracted from data, how signal and background are estimated, and how accurate error estimates are obtained exploiting uni- and multivariate analysis techniques. The book includes simple code snippets that run on the popular software suite MATLAB. These snippets make use of publicly available datasets that can be downloaded from the Web.

Primarily aimed at PhD and very advanced undergraduate students, this text can be also used by researchers.

From the contents:

• Parametric likelihood fits
• Goodness of fit
• Resampling techniques
• Density estimation
• Data pre-processing
• Linear transformations and dimensionality reduction
• Introduction to classifi cation
• Assessing classifi er performance
• Linear classification
• Neural networks
• Local learning and kernel expansion
• Decision trees
• Ensemble learning
• Reducing multiclass to binary
• Methods for variable ranking and selection

Details zur Ausgabe

• Verlag: Wiley-VCH
• Erscheinungsdatum: 2013
• Sprache: Englisch
• ISBN 10: 3527410864
• ISBN 13: 9783527410866
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 459
• Kontakt zum Hersteller: Wiley-VCH GmbH; product_safety@wiley.com; ; Boschstrasse 12; Weinheim; 69469; Deutschland