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OverviewPhysicists are conducting increasingly complex experiments in the hope of advancing our understanding of the universe. To fully exploit the discovery potential of these experiments it will be crucial to use optimal methods of data analysis. Since physical processes are generally characterized by many variables, optimal methods are necessarily multivariate, and neural networks are the most promising of them. These have the potential to revolutionize data exploration in high energy physics -- witness, as a recent example, the spectacular success with which neural networks have been used in top quark physics and searches for new particles.This book draws together the dispersed expertise of the scientific community to provide a unified, coherent and practical exposition of multivariate methods as they are (and will be) applied. It is a timely reference for high energy physicists and researchers in related fields who need a concise introduction to the subject. The first three chapters contain an exposition of the main concepts of multivariate methods, while the remaining chapters focus on applications using real examples. A convincing case is made that neural networks will be the method of choice in future analyses. Full Product DetailsAuthor: Harrison B Prosper (Florida State Univ, Usa) , Pushpalatha C Bhat (Fermi Nat'l Accelerator Lab, Usa & Northern Illinois Univ, Usa)Publisher: World Scientific Publishing Co Pte Ltd Imprint: World Scientific Publishing Co Pte Ltd ISBN: 9789810243470ISBN 10: 9810243472 Pages: 300 Publication Date: 29 February 2020 Audience: College/higher education , Professional and scholarly , Tertiary & Higher Education , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Awaiting stock The supplier is currently out of stock of this item. It will be ordered for you and placed on backorder. Once it does come back in stock, we will ship it out for you. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |