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Statistical analysis techniques in particle physics : fits, density estimation and supervised learning / / Ilya Narsky and Frank C. Porter
| Statistical analysis techniques in particle physics : fits, density estimation and supervised learning / / Ilya Narsky and Frank C. Porter |
| Autore | Narsky Ilya |
| Pubbl/distr/stampa | Weinheim : , : Wiley-VCH, , [2014] |
| Descrizione fisica | 1 online resource (461 p.) |
| Disciplina | 530.4 |
| Soggetto topico |
Particles (Nuclear physics) - Statistical methods
Physics Condensed matter |
| ISBN |
3-527-67729-1
3-527-67732-1 3-527-67731-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistical 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
2.3.1 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 3.4.5 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 5.6.1 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 7.1 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 8.4 Independent Component Analysis (ICA) |
| Record Nr. | UNINA-9910138994803321 |
Narsky Ilya
|
||
| Weinheim : , : Wiley-VCH, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical analysis techniques in particle physics : fits, density estimation and supervised learning / / Ilya Narsky and Frank C. Porter
| Statistical analysis techniques in particle physics : fits, density estimation and supervised learning / / Ilya Narsky and Frank C. Porter |
| Autore | Narsky Ilya |
| Pubbl/distr/stampa | Weinheim : , : Wiley-VCH, , [2014] |
| Descrizione fisica | 1 online resource (461 p.) |
| Disciplina | 530.4 |
| Soggetto topico |
Particles (Nuclear physics) - Statistical methods
Physics Condensed matter |
| ISBN |
3-527-67729-1
3-527-67732-1 3-527-67731-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistical 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
2.3.1 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 3.4.5 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 5.6.1 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 7.1 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 8.4 Independent Component Analysis (ICA) |
| Record Nr. | UNINA-9910824536103321 |
|
Narsky Ilya
|
||
| Weinheim : , : Wiley-VCH, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||