LEADER 04977nam 2200625 450 001 9910138994803321 005 20230803220247.0 010 $a3-527-67729-1 010 $a3-527-67732-1 010 $a3-527-67731-3 035 $a(CKB)2550000001134429 035 $a(EBL)1486350 035 $a(OCoLC)861559452 035 $a(MiAaPQ)EBC1486350 035 $a(Au-PeEL)EBL1486350 035 $a(CaPaEBR)ebr10925517 035 $a(CaONFJC)MIL534137 035 $a(EXLCZ)992550000001134429 100 $a20140906h20142014 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aStatistical analysis techniques in particle physics $efits, density estimation and supervised learning /$fIlya Narsky and Frank C. Porter 210 1$aWeinheim :$cWiley-VCH,$d[2014] 210 4$dİ2014 215 $a1 online resource (461 p.) 300 $aDescription based upon print version of record. 311 $a3-527-41086-4 311 $a1-306-02886-8 320 $aIncludes bibliographical references and index. 327 $aStatistical Analysis Techniques in Particle Physics; Contents; Acknowledgements; Notation and Vocabulary; 1 Why We Wrote This Book and How You Should Read It; 2 Parametric Likelihood Fits; 2.1 Preliminaries; 2.1.1 Example: CP Violation via Mixing; 2.1.2 The Exponential Family; 2.1.3 Confidence Intervals; 2.1.4 Hypothesis Tests; 2.2 Parametric Likelihood Fits; 2.2.1 Nuisance Parameters; 2.2.2 Confidence Intervals from Pivotal Quantities; 2.2.3 Asymptotic Inference; 2.2.4 Profile Likelihood; 2.2.5 Conditional Likelihood; 2.3 Fits for Small Statistics 327 $a2.3.1 Sample Study of Coverage at Small Statistics2.3.2 When the pdf Goes Negative; 2.4 Results Near the Boundary of a Physical Region; 2.5 Likelihood Ratio Test for Presence of Signal; 2.6 sPlots; 2.7 Exercises; References; 3 Goodness of Fit; 3.1 Binned Goodness of Fit Tests; 3.2 Statistics Converging to Chi-Square; 3.3 Univariate Unbinned Goodness of Fit Tests; 3.3.1 Kolmogorov-Smirnov; 3.3.2 Anderson-Darling; 3.3.3 Watson; 3.3.4 Neyman Smooth; 3.4 Multivariate Tests; 3.4.1 Energy Tests; 3.4.2 Transformations to a Uniform Distribution; 3.4.3 Local Density Tests; 3.4.4 Kernel-based Tests 327 $a3.4.5 Mixed Sample Tests3.4.6 Using a Classifier; 3.5 Exercises; References; 4 Resampling Techniques; 4.1 Permutation Sampling; 4.2 Bootstrap; 4.2.1 Bootstrap Confidence Intervals; 4.2.2 Smoothed Bootstrap; 4.2.3 Parametric Bootstrap; 4.3 Jackknife; 4.4 BCa Confidence Intervals; 4.5 Cross-Validation; 4.6 Resampling Weighted Observations; 4.7 Exercises; References; 5 Density Estimation; 5.1 Empirical Density Estimate; 5.2 Histograms; 5.3 Kernel Estimation; 5.3.1 Multivariate Kernel Estimation; 5.4 Ideogram; 5.5 Parametric vs. Nonparametric Density Estimation; 5.6 Optimization 327 $a5.6.1 Choosing Histogram Binning5.7 Estimating Errors; 5.8 The Curse of Dimensionality; 5.9 Adaptive Kernel Estimation; 5.10 Naive Bayes Classification; 5.11 Multivariate Kernel Estimation; 5.12 Estimation Using Orthogonal Series; 5.13 Using Monte Carlo Models; 5.14 Unfolding; 5.14.1 Unfolding: Regularization; 5.15 Exercises; References; 6 Basic Concepts and Definitions of Machine Learning; 6.1 Supervised, Unsupervised, and Semi-Supervised; 6.2 Tall and Wide Data; 6.3 Batch and Online Learning; 6.4 Parallel Learning; 6.5 Classification and Regression; References; 7 Data Preprocessing 327 $a7.1 Categorical Variables7.2 Missing Values; 7.2.1 Likelihood Optimization; 7.2.2 Deletion; 7.2.3 Augmentation; 7.2.4 Imputation; 7.2.5 Other Methods; 7.3 Outliers; 7.4 Exercises; References; 8 Linear Transformations and Dimensionality Reduction; 8.1 Centering, Scaling, Reflection and Rotation; 8.2 Rotation and Dimensionality Reduction; 8.3 Principal Component Analysis (PCA); 8.3.1 Theory; 8.3.2 Numerical Implementation; 8.3.3 Weighted Data; 8.3.4 How Many Principal Components Are Enough?; 8.3.5 Example: Apply PCA and Choose the Optimal Number of Components 327 $a8.4 Independent Component Analysis (ICA) 330 $aModern 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. 606 $aParticles (Nuclear physics)$xStatistical methods 606 $aPhysics 606 $aCondensed matter 615 0$aParticles (Nuclear physics)$xStatistical methods. 615 0$aPhysics. 615 0$aCondensed matter. 676 $a530.4 700 $aNarsky$b Ilya$0911821 702 $aPorter$b Frank C. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910138994803321 996 $aStatistical analysis techniques in particle physics$92041945 997 $aUNINA