Statistical Analysis Techniques in Particle Physics
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A01=Frank C. Porter
A01=Ilya Narsky
Author_Frank C. Porter
Author_Ilya Narsky
Category=PBT
Category=PHP
Category=PHU
decision tress
ensembles of classifiers
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
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error estimates
high-energy physicists
likelihood fits
Matlab
neural networks
non-parametric density estimation
particle physics
Root
Statistical analysis techniques
StatPatterRecognition
support vector machines
Product details
- ISBN 9783527410866
- Weight: 980g
- Dimensions: 170 x 240mm
- Publication Date: 13 Nov 2013
- Publisher: Wiley-VCH Verlag GmbH
- Publication City/Country: DE
- Product Form: Paperback
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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.
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.
