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Data analysis in high energy physics [[electronic resource]] : a practical guide to statistical methods / / edited by Olaf Behnke ... [et al.]
| Data analysis in high energy physics [[electronic resource]] : a practical guide to statistical methods / / edited by Olaf Behnke ... [et al.] |
| Pubbl/distr/stampa | Weinheim an der Bergstrasse, Germany, : Wiley-VCH Verlag GmbH, c2013 |
| Descrizione fisica | 1 online resource (441 p.) |
| Disciplina | 539.760285 |
| Altri autori (Persone) | BehnkeOlaf |
| Soggetto topico | Particles (Nuclear physics) - Statistical methods |
| ISBN |
3-527-65343-0
3-527-65341-4 3-527-65344-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Data Analysis in High Energy Physics; Contents; Preface; List of Contributors; 1 Fundamental Concepts; 1.1 Introduction; 1.2 Probability Density Functions; 1.2.1 Expectation Values; 1.2.2 Moments; 1.2.3 Associated Functions; 1.3 Theoretical Distributions; 1.3.1 The Gaussian Distribution; 1.3.2 The Poisson Distribution; 1.3.3 The Binomial Distribution; 1.3.4 Other Distributions; 1.4 Probability; 1.4.1 Mathematical Definition of Probability; 1.4.2 Classical Definition of Probability; 1.4.3 Frequentist Definition of Probability; 1.4.4 Bayesian Definition of Probability
1.5 Inference and Measurement1.5.1 Likelihood; 1.5.2 Frequentist Inference; 1.5.3 Bayesian Inference; 1.6 Exercises; References; 2 Parameter Estimation; 2.1 Parameter Estimation in High Energy Physics: Introductory Words; 2.2 Parameter Estimation: Definition and Properties; 2.3 The Method of Maximum Likelihood; 2.3.1 Maximum-Likelihood Solution; 2.3.2 Properties of the Maximum-Likelihood Estimator; 2.3.3 Maximum Likelihood and Bayesian Statistics; 2.3.4 Variance of the Maximum-Likelihood Estimator; 2.3.5 Minimum-Variance Bound and Experiment Design; 2.4 The Method of Least Squares 2.4.1 Linear Least-Squares Method2.4.2 Non-linear Least-Squares Fits; 2.5 Maximum-Likelihood Fits:Unbinned, Binned, Standard and Extended Likelihood; 2.5.1 Unbinned Maximum-Likelihood Fits; 2.5.2 Extended Maximum Likelihood; 2.5.3 Binned Maximum-Likelihood Fits; 2.5.4 Least-Squares Fit to a Histogram; 2.5.5 Special Topic: Averaging Data with Inconsistencies; 2.6 Bayesian Parameter Estimation; 2.7 Exercises; References; 3 Hypothesis Testing; 3.1 Basic Concepts; 3.1.1 Statistical Hypotheses; 3.1.2 Test Statistic; 3.1.3 Critical Region; 3.1.4 Type I and Type II Errors 3.1.5 Summary: the Testing Process3.2 Choosing the Test Statistic; 3.3 Choice of the Critical Region; 3.4 Determining Test Statistic Distributions; 3.5 p-Values; 3.5.1 Significance Levels; 3.5.2 Inclusion of Systematic Uncertainties; 3.5.3 Combining Tests; 3.5.4 Look-Elsewhere Effect; 3.6 Inversion of Hypothesis Tests; 3.7 Bayesian Approach to Hypothesis Testing; 3.8 Goodness-of-Fit Tests; 3.8.1 Pearson's 2 Test; 3.8.2 Run Test; 3.8.3 2 Test with Unbinned Measurements; 3.8.4 Test Using the Maximum-Likelihood Estimate; 3.8.5 Kolmogorov-Smirnov Test; 3.8.6 Smirnov-Cramér-von Mises Test 3.8.7 Two-Sample Tests3.9 Conclusion; 3.10 Exercises; References; 4 Interval Estimation; 4.1 Introduction; 4.2 Characterisation of Interval Constructions; 4.3 Frequentist Methods; 4.3.1 Neyman's Construction; 4.3.2 Test Inversion; 4.3.3 Pivoting; 4.3.4 Asymptotic Approximations; 4.3.5 Bootstrapping; 4.3.6 Nuisance Parameters; 4.4 Bayesian Methods; 4.4.1 Binomial Efficiencies; 4.4.2 Poisson Means; 4.5 Graphical Comparison of Interval Constructions; 4.6 The Role of Intervals in Search Procedures; 4.6.1 Coverage; 4.6.2 Sensitivity; 4.7 Final Remarks and Recommendations; 4.8 Exercises; References 5 Classification |
| Record Nr. | UNINA-9910139045803321 |
| Weinheim an der Bergstrasse, Germany, : Wiley-VCH Verlag GmbH, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data analysis in high energy physics : a practical guide to statistical methods / / edited by Olaf Behnke ... [et al.]
| Data analysis in high energy physics : a practical guide to statistical methods / / edited by Olaf Behnke ... [et al.] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Weinheim an der Bergstrasse, Germany, : Wiley-VCH Verlag GmbH, c2013 |
| Descrizione fisica | 1 online resource (441 p.) |
| Disciplina | 539.760285 |
| Altri autori (Persone) | BehnkeOlaf |
| Soggetto topico | Particles (Nuclear physics) - Statistical methods |
| ISBN |
3-527-65343-0
3-527-65341-4 3-527-65344-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Data Analysis in High Energy Physics; Contents; Preface; List of Contributors; 1 Fundamental Concepts; 1.1 Introduction; 1.2 Probability Density Functions; 1.2.1 Expectation Values; 1.2.2 Moments; 1.2.3 Associated Functions; 1.3 Theoretical Distributions; 1.3.1 The Gaussian Distribution; 1.3.2 The Poisson Distribution; 1.3.3 The Binomial Distribution; 1.3.4 Other Distributions; 1.4 Probability; 1.4.1 Mathematical Definition of Probability; 1.4.2 Classical Definition of Probability; 1.4.3 Frequentist Definition of Probability; 1.4.4 Bayesian Definition of Probability
1.5 Inference and Measurement1.5.1 Likelihood; 1.5.2 Frequentist Inference; 1.5.3 Bayesian Inference; 1.6 Exercises; References; 2 Parameter Estimation; 2.1 Parameter Estimation in High Energy Physics: Introductory Words; 2.2 Parameter Estimation: Definition and Properties; 2.3 The Method of Maximum Likelihood; 2.3.1 Maximum-Likelihood Solution; 2.3.2 Properties of the Maximum-Likelihood Estimator; 2.3.3 Maximum Likelihood and Bayesian Statistics; 2.3.4 Variance of the Maximum-Likelihood Estimator; 2.3.5 Minimum-Variance Bound and Experiment Design; 2.4 The Method of Least Squares 2.4.1 Linear Least-Squares Method2.4.2 Non-linear Least-Squares Fits; 2.5 Maximum-Likelihood Fits:Unbinned, Binned, Standard and Extended Likelihood; 2.5.1 Unbinned Maximum-Likelihood Fits; 2.5.2 Extended Maximum Likelihood; 2.5.3 Binned Maximum-Likelihood Fits; 2.5.4 Least-Squares Fit to a Histogram; 2.5.5 Special Topic: Averaging Data with Inconsistencies; 2.6 Bayesian Parameter Estimation; 2.7 Exercises; References; 3 Hypothesis Testing; 3.1 Basic Concepts; 3.1.1 Statistical Hypotheses; 3.1.2 Test Statistic; 3.1.3 Critical Region; 3.1.4 Type I and Type II Errors 3.1.5 Summary: the Testing Process3.2 Choosing the Test Statistic; 3.3 Choice of the Critical Region; 3.4 Determining Test Statistic Distributions; 3.5 p-Values; 3.5.1 Significance Levels; 3.5.2 Inclusion of Systematic Uncertainties; 3.5.3 Combining Tests; 3.5.4 Look-Elsewhere Effect; 3.6 Inversion of Hypothesis Tests; 3.7 Bayesian Approach to Hypothesis Testing; 3.8 Goodness-of-Fit Tests; 3.8.1 Pearson's 2 Test; 3.8.2 Run Test; 3.8.3 2 Test with Unbinned Measurements; 3.8.4 Test Using the Maximum-Likelihood Estimate; 3.8.5 Kolmogorov-Smirnov Test; 3.8.6 Smirnov-Cramér-von Mises Test 3.8.7 Two-Sample Tests3.9 Conclusion; 3.10 Exercises; References; 4 Interval Estimation; 4.1 Introduction; 4.2 Characterisation of Interval Constructions; 4.3 Frequentist Methods; 4.3.1 Neyman's Construction; 4.3.2 Test Inversion; 4.3.3 Pivoting; 4.3.4 Asymptotic Approximations; 4.3.5 Bootstrapping; 4.3.6 Nuisance Parameters; 4.4 Bayesian Methods; 4.4.1 Binomial Efficiencies; 4.4.2 Poisson Means; 4.5 Graphical Comparison of Interval Constructions; 4.6 The Role of Intervals in Search Procedures; 4.6.1 Coverage; 4.6.2 Sensitivity; 4.7 Final Remarks and Recommendations; 4.8 Exercises; References 5 Classification |
| Record Nr. | UNINA-9910824355203321 |
| Weinheim an der Bergstrasse, Germany, : Wiley-VCH Verlag GmbH, c2013 | ||
| 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-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 | ||
| ||
