Multivariate Methods in High Energy Physics
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Multivariate Methods in High Energy Physics by Pushpalatha C. Bhat

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Published by World Scientific Publishing Company .
Written in English

Subjects:

  • Nuclear Physics,
  • Solid State Physics,
  • Physics,
  • Probability & Statistics - General,
  • High Energy Physics,
  • Multivariate Analysis,
  • Science,
  • Science/Mathematics

Book details:

The Physical Object
FormatHardcover
Number of Pages300
ID Numbers
Open LibraryOL13168174M
ISBN 109810243472
ISBN 109789810243470

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MULTIVARIATE ANALYSIS AND ITS USE IN HIGH ENERGY PHYSICS: OPTIMISATION INTRODUCTION We initialise the weights of a neural network or other MVA algorithm randomly. The determination a set of optimal weights requires some heuristic algorithm and some figure of merit. The algorithm is the optimisation method (typically derived from. An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field. Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional.   Get up-to-speed on the latest methods of multivariate statistics Multivariate statistical methods provide a powerful tool for analyzing data when observations are taken over a period of time on the same subject. With the advent of fast and efficient computers and the availability of computer packages such as S-plus and SAS, multivariate methods once too complex to tackle are now within Format: Hardcover. Marcin Wolter: “Multivariate Analysis Methods” 6 Independent Component Analysis • Novel technique – Helsinki University of Technology • Basic Idea • Assume X = (x1,..,xN)T is a linear sum X = AS of independent sources S = (s1,..,sN) A, the mixing matrix, and S are unknown. • Find a de-mixing matrix T such that the components of.

This book focuses on when to use the various analytic techniques and how to interpret the resulting output from the most widely used statistical packages (e.g., SAS, SPSS). Nuclear & High Energy Physics Optics & Photonics Particle Physics Physics Special Topics Subhash Sharma is the author of Applied Multivariate Techniques, published. This paper compares five different methods for selecting the most important variables with a view to classify high energy physics events with neural networks. The different methods are: the F-test, principal component analysis (PCA), a decision tree method: . Amstat News asked three review editors to rate their top five favorite books in the September issue. Methods of Multivariate Analysis was among those chosen. When measuring several variables on a complex experimental unit, it is often necessary to analyze the variables simultaneously, rather than isolate them and consider them individually. The content of the lecture is roughly separated into two parts. The first part focuses on traditional methods used for multivariate classification in High Energy Physics (excluding neural networks). The second part introduces neural networks and recent developments in the field of Deep Learning.

_ Statistical methods commonly used in high energy physics _ Analysis walk-throughs _ Applications in astronomy. From the Back Cover. This practical guide covers the essential tasks in statistical data analysis encountered in high energy physics and provides comprehensive advice for typical questions and problems. The basic methods for Reviews: 6. optimization of tau identification in atlas experiment using multivariate tools Elementary particle physics experiments, which search for very rare processes, require theefficient analysis and selection algorithms able to separate a signal from the overwhelmingbackground. Lecture 2: Multivariate Methods CERN­JINR European School of High Energy Physics Bautzen, 14–27 June Glen Cowan Physics Department Royal Holloway, University of London @ Some references for kernel methods and SVMs: The books mentioned in. in this book got their start in the multivariate course I took from him forty years ago. I think they have aged well. Also, thanks to Steen Andersson, from whom I learned a lot, including the idea that one should define a model before trying to analyze it. This book is dedicated to Ann.