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Statistical analysis techniques in particle physics : fits, density estimation and supervised learning / Ilya Narsky and Frank C. Porter.

By: Contributor(s): Publication details: Germany : Wiley-VCH, c2014.Description: xvii, 441 p. ; illustrationsISBN:
  • 9783527410866
Subject(s): DDC classification:
  • 23 N235 000SB:539.72
Contents:
1. Why We Wrote This Book and How You Should Read It -- 2. Parametric Likelihood Fits -- 3. Goodness of Fit -- 4. Resampling Techniques -- 5. Density Estimation -- 6. Basic Concepts and Definitions of Machine Learning -- 7. Data Preprocessing -- 8. Linear Transformations and Dimensionality Reduction -- 9. Introduction to Classification -- 10. Assessing Classifier Performance -- 11. Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression -- 12. Neural Networks -- 13. Local Learning and Kernel Expansion -- 14. Decision Trees -- 15. Ensemble Learning -- 16. Reducing Multiclass to Binary -- 17. How to Choose the Right Classifier for Your Analysis and Apply It Correctly -- 18. Methods for Variable Ranking and Selection -- 19. Bump Hunting in Multivariate Data -- 20. Software Packages for Machine Learning -- Appendix A: Optimization Algorithms-- Index.
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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SB:539.72 N235 (Browse shelf(Opens below)) Available 135737
Total holds: 0

Includes index.

1. Why We Wrote This Book and How You Should Read It --
2. Parametric Likelihood Fits --
3. Goodness of Fit --
4. Resampling Techniques --
5. Density Estimation --
6. Basic Concepts and Definitions of Machine Learning --
7. Data Preprocessing --
8. Linear Transformations and Dimensionality Reduction --
9. Introduction to Classification --
10. Assessing Classifier Performance --
11. Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression --
12. Neural Networks --
13. Local Learning and Kernel Expansion --
14. Decision Trees --
15. Ensemble Learning --
16. Reducing Multiclass to Binary --
17. How to Choose the Right Classifier for Your Analysis and Apply It Correctly --
18. Methods for Variable Ranking and Selection --
19. Bump Hunting in Multivariate Data --
20. Software Packages for Machine Learning --
Appendix A: Optimization Algorithms--
Index.

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.

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