Statistics, data mining, and machine learning in astronomy: a practical Python guide for the analysis of survey data
Saved in:
| Format: | Electronic eBook |
|---|---|
| Language: | English |
| Published: |
Princeton, N.J.
Princeton University Press
2014
|
| Series: | Princeton series in modern observational astronomy
|
| Subjects: | |
| Online Access: | FAW01 FAW02 Volltext |
| Item Description: | 8.3 Regularization and Penalizing the Likelihood Print version record |
| Physical Description: | 1 online resource (x, 540 pages) illustrations |
| ISBN: | 0691151687 1306373840 1400848911 9780691151687 9781306373845 9781400848911 |
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MARC
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| 245 | 1 | 0 | |a Statistics, data mining, and machine learning in astronomy |b a practical Python guide for the analysis of survey data |c Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray |
| 264 | 1 | |a Princeton, N.J. |b Princeton University Press |c 2014 | |
| 264 | 4 | |c © 2014 | |
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| 505 | 8 | |a Cover; Title; Copyright; Contents; Preface; I Introduction; 1 About the Book and Supporting Material; 1.1 What Do Data Mining, Machine Learning, and Knowledge Discovery Mean?; 1.2 What is This Book About?; 1.3 An Incomplete Survey of the Relevant Literature; 1.4 Introduction to the Python Language and the Git Code Management Tool; 1.5 Description of Surveys and Data Sets Used in Examples; 1.6 Plotting and Visualizing the Data in This Book; 1.7 How to Efficiently Use This Book; References; 2 Fast Computation on Massive Data Sets; 2.1 Data Types and Data Management Systems | |
| 505 | 8 | |a 2.2 Analysis of Algorithmic Efficiency2.3 Seven Types of Computational Problem; 2.4 Seven Strategies for Speeding Things Up; 2.5 Case Studies: Speedup Strategies in Practice; References; II Statistical Frameworks and Exploratory Data Analysis; 3 Probability and Statistical Distributions; 3.1 Brief Overview of Probability and Random Variables; 3.2 Descriptive Statistics; 3.3 Common Univariate Distribution Functions; 3.4 The Central Limit Theorem; 3.5 Bivariate and Multivariate Distribution Functions; 3.6 Correlation Coefficients; 3.7 Random Number Generation for Arbitrary Distributions | |
| 505 | 8 | |a 5.3 Bayesian Parameter Uncertainty Quantification5.4 Bayesian Model Selection; 5.5 Nonuniform Priors: Eddington, Malmquist, and Lutz-Kelker Biases; 5.6 Simple Examples of Bayesian Analysis: Parameter Estimation; 5.7 Simple Examples of Bayesian Analysis: Model Selection; 5.8 Numerical Methods for Complex Problems (MCMC); 5.9 Summary of Pros and Cons for Classical and Bayesian methods; References; III Data Mining and Machine Learning; 6 Searching for Structure in Point Data; 6.1 Nonparametric Density Estimation; 6.2 Nearest-Neighbor Density Estimation; 6.3 Parametric Density Estimation | |
| 505 | 8 | |a 6.4 Finding Clusters in Data6.5 Correlation Functions; 6.6 Which Density Estimation and Clustering Algorithms Should I Use?; References; 7 Dimensionality and Its Reduction; 7.1 The Curse of Dimensionality; 7.2 The Data Sets Used in This Chapter; 7.3 Principal Component Analysis; 7.4 Nonnegative Matrix Factorization; 7.5 Manifold Learning; 7.6 Independent Component Analysis and Projection Pursuit; 7.7 Which Dimensionality Reduction Technique Should I Use?; References; 8 Regression and Model Fitting; 8.1 Formulation of the Regression Problem; 8.2 Regression for Linear Models | |
| 505 | 8 | |a As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate | |
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| contents | Cover; Title; Copyright; Contents; Preface; I Introduction; 1 About the Book and Supporting Material; 1.1 What Do Data Mining, Machine Learning, and Knowledge Discovery Mean?; 1.2 What is This Book About?; 1.3 An Incomplete Survey of the Relevant Literature; 1.4 Introduction to the Python Language and the Git Code Management Tool; 1.5 Description of Surveys and Data Sets Used in Examples; 1.6 Plotting and Visualizing the Data in This Book; 1.7 How to Efficiently Use This Book; References; 2 Fast Computation on Massive Data Sets; 2.1 Data Types and Data Management Systems 2.2 Analysis of Algorithmic Efficiency2.3 Seven Types of Computational Problem; 2.4 Seven Strategies for Speeding Things Up; 2.5 Case Studies: Speedup Strategies in Practice; References; II Statistical Frameworks and Exploratory Data Analysis; 3 Probability and Statistical Distributions; 3.1 Brief Overview of Probability and Random Variables; 3.2 Descriptive Statistics; 3.3 Common Univariate Distribution Functions; 3.4 The Central Limit Theorem; 3.5 Bivariate and Multivariate Distribution Functions; 3.6 Correlation Coefficients; 3.7 Random Number Generation for Arbitrary Distributions 5.3 Bayesian Parameter Uncertainty Quantification5.4 Bayesian Model Selection; 5.5 Nonuniform Priors: Eddington, Malmquist, and Lutz-Kelker Biases; 5.6 Simple Examples of Bayesian Analysis: Parameter Estimation; 5.7 Simple Examples of Bayesian Analysis: Model Selection; 5.8 Numerical Methods for Complex Problems (MCMC); 5.9 Summary of Pros and Cons for Classical and Bayesian methods; References; III Data Mining and Machine Learning; 6 Searching for Structure in Point Data; 6.1 Nonparametric Density Estimation; 6.2 Nearest-Neighbor Density Estimation; 6.3 Parametric Density Estimation 6.4 Finding Clusters in Data6.5 Correlation Functions; 6.6 Which Density Estimation and Clustering Algorithms Should I Use?; References; 7 Dimensionality and Its Reduction; 7.1 The Curse of Dimensionality; 7.2 The Data Sets Used in This Chapter; 7.3 Principal Component Analysis; 7.4 Nonnegative Matrix Factorization; 7.5 Manifold Learning; 7.6 Independent Component Analysis and Projection Pursuit; 7.7 Which Dimensionality Reduction Technique Should I Use?; References; 8 Regression and Model Fitting; 8.1 Formulation of the Regression Problem; 8.2 Regression for Linear Models As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate |
| ctrlnum | (OCoLC)869091101 (DE-599)BVBBV043039355 |
| dewey-full | 006.312 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.312 |
| dewey-search | 006.312 |
| dewey-sort | 16.312 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Physik Informatik |
| format | Electronic eBook |
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| id | DE-604.BV043039355 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:15:43Z |
| institution | BVB |
| isbn | 0691151687 1306373840 1400848911 9780691151687 9781306373845 9781400848911 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028464002 |
| oclc_num | 869091101 |
| open_access_boolean | |
| owner | DE-1046 DE-1047 |
| owner_facet | DE-1046 DE-1047 |
| physical | 1 online resource (x, 540 pages) illustrations |
| psigel | ZDB-4-EBA ZDB-4-EBA FAW_PDA_EBA |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Princeton University Press |
| record_format | marc |
| series2 | Princeton series in modern observational astronomy |
| spelling | Statistics, data mining, and machine learning in astronomy a practical Python guide for the analysis of survey data Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray Princeton, N.J. Princeton University Press 2014 © 2014 1 online resource (x, 540 pages) illustrations txt rdacontent c rdamedia cr rdacarrier Princeton series in modern observational astronomy 8.3 Regularization and Penalizing the Likelihood Print version record Cover; Title; Copyright; Contents; Preface; I Introduction; 1 About the Book and Supporting Material; 1.1 What Do Data Mining, Machine Learning, and Knowledge Discovery Mean?; 1.2 What is This Book About?; 1.3 An Incomplete Survey of the Relevant Literature; 1.4 Introduction to the Python Language and the Git Code Management Tool; 1.5 Description of Surveys and Data Sets Used in Examples; 1.6 Plotting and Visualizing the Data in This Book; 1.7 How to Efficiently Use This Book; References; 2 Fast Computation on Massive Data Sets; 2.1 Data Types and Data Management Systems 2.2 Analysis of Algorithmic Efficiency2.3 Seven Types of Computational Problem; 2.4 Seven Strategies for Speeding Things Up; 2.5 Case Studies: Speedup Strategies in Practice; References; II Statistical Frameworks and Exploratory Data Analysis; 3 Probability and Statistical Distributions; 3.1 Brief Overview of Probability and Random Variables; 3.2 Descriptive Statistics; 3.3 Common Univariate Distribution Functions; 3.4 The Central Limit Theorem; 3.5 Bivariate and Multivariate Distribution Functions; 3.6 Correlation Coefficients; 3.7 Random Number Generation for Arbitrary Distributions 5.3 Bayesian Parameter Uncertainty Quantification5.4 Bayesian Model Selection; 5.5 Nonuniform Priors: Eddington, Malmquist, and Lutz-Kelker Biases; 5.6 Simple Examples of Bayesian Analysis: Parameter Estimation; 5.7 Simple Examples of Bayesian Analysis: Model Selection; 5.8 Numerical Methods for Complex Problems (MCMC); 5.9 Summary of Pros and Cons for Classical and Bayesian methods; References; III Data Mining and Machine Learning; 6 Searching for Structure in Point Data; 6.1 Nonparametric Density Estimation; 6.2 Nearest-Neighbor Density Estimation; 6.3 Parametric Density Estimation 6.4 Finding Clusters in Data6.5 Correlation Functions; 6.6 Which Density Estimation and Clustering Algorithms Should I Use?; References; 7 Dimensionality and Its Reduction; 7.1 The Curse of Dimensionality; 7.2 The Data Sets Used in This Chapter; 7.3 Principal Component Analysis; 7.4 Nonnegative Matrix Factorization; 7.5 Manifold Learning; 7.6 Independent Component Analysis and Projection Pursuit; 7.7 Which Dimensionality Reduction Technique Should I Use?; References; 8 Regression and Model Fitting; 8.1 Formulation of the Regression Problem; 8.2 Regression for Linear Models As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate Astronomy Data analysis Data mining COMPUTERS / General bisacsh SCIENCE / Physics / Mathematical & Computational bisacsh Astronomy / Data processing fast Statistical astronomy fast Datenverarbeitung Astronomy Data processing Statistical astronomy Datenanalyse (DE-588)4123037-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 gnd rswk-swf Astronomie (DE-588)4003311-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Statistik (DE-588)4056995-0 s Data Mining (DE-588)4428654-5 s Maschinelles Lernen (DE-588)4193754-5 s Astronomie (DE-588)4003311-9 s DE-604 Datenanalyse (DE-588)4123037-1 s Statistische Schlussweise (DE-588)4182963-3 s 1\p DE-604 Ivezić, Željko Sonstige oth Connolly, Andrew Sonstige oth VanderPlas, Jacob T. Sonstige oth Gray, Alexander Sonstige oth Erscheint auch als Druck-Ausgabe Statistics, data mining, and machine learning in astronomy http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=650266 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Statistics, data mining, and machine learning in astronomy a practical Python guide for the analysis of survey data Cover; Title; Copyright; Contents; Preface; I Introduction; 1 About the Book and Supporting Material; 1.1 What Do Data Mining, Machine Learning, and Knowledge Discovery Mean?; 1.2 What is This Book About?; 1.3 An Incomplete Survey of the Relevant Literature; 1.4 Introduction to the Python Language and the Git Code Management Tool; 1.5 Description of Surveys and Data Sets Used in Examples; 1.6 Plotting and Visualizing the Data in This Book; 1.7 How to Efficiently Use This Book; References; 2 Fast Computation on Massive Data Sets; 2.1 Data Types and Data Management Systems 2.2 Analysis of Algorithmic Efficiency2.3 Seven Types of Computational Problem; 2.4 Seven Strategies for Speeding Things Up; 2.5 Case Studies: Speedup Strategies in Practice; References; II Statistical Frameworks and Exploratory Data Analysis; 3 Probability and Statistical Distributions; 3.1 Brief Overview of Probability and Random Variables; 3.2 Descriptive Statistics; 3.3 Common Univariate Distribution Functions; 3.4 The Central Limit Theorem; 3.5 Bivariate and Multivariate Distribution Functions; 3.6 Correlation Coefficients; 3.7 Random Number Generation for Arbitrary Distributions 5.3 Bayesian Parameter Uncertainty Quantification5.4 Bayesian Model Selection; 5.5 Nonuniform Priors: Eddington, Malmquist, and Lutz-Kelker Biases; 5.6 Simple Examples of Bayesian Analysis: Parameter Estimation; 5.7 Simple Examples of Bayesian Analysis: Model Selection; 5.8 Numerical Methods for Complex Problems (MCMC); 5.9 Summary of Pros and Cons for Classical and Bayesian methods; References; III Data Mining and Machine Learning; 6 Searching for Structure in Point Data; 6.1 Nonparametric Density Estimation; 6.2 Nearest-Neighbor Density Estimation; 6.3 Parametric Density Estimation 6.4 Finding Clusters in Data6.5 Correlation Functions; 6.6 Which Density Estimation and Clustering Algorithms Should I Use?; References; 7 Dimensionality and Its Reduction; 7.1 The Curse of Dimensionality; 7.2 The Data Sets Used in This Chapter; 7.3 Principal Component Analysis; 7.4 Nonnegative Matrix Factorization; 7.5 Manifold Learning; 7.6 Independent Component Analysis and Projection Pursuit; 7.7 Which Dimensionality Reduction Technique Should I Use?; References; 8 Regression and Model Fitting; 8.1 Formulation of the Regression Problem; 8.2 Regression for Linear Models As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate Astronomy Data analysis Data mining COMPUTERS / General bisacsh SCIENCE / Physics / Mathematical & Computational bisacsh Astronomy / Data processing fast Statistical astronomy fast Datenverarbeitung Astronomy Data processing Statistical astronomy Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Astronomie (DE-588)4003311-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4056995-0 (DE-588)4428654-5 (DE-588)4182963-3 (DE-588)4003311-9 (DE-588)4193754-5 |
| title | Statistics, data mining, and machine learning in astronomy a practical Python guide for the analysis of survey data |
| title_auth | Statistics, data mining, and machine learning in astronomy a practical Python guide for the analysis of survey data |
| title_exact_search | Statistics, data mining, and machine learning in astronomy a practical Python guide for the analysis of survey data |
| title_full | Statistics, data mining, and machine learning in astronomy a practical Python guide for the analysis of survey data Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray |
| title_fullStr | Statistics, data mining, and machine learning in astronomy a practical Python guide for the analysis of survey data Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray |
| title_full_unstemmed | Statistics, data mining, and machine learning in astronomy a practical Python guide for the analysis of survey data Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray |
| title_short | Statistics, data mining, and machine learning in astronomy |
| title_sort | statistics data mining and machine learning in astronomy a practical python guide for the analysis of survey data |
| title_sub | a practical Python guide for the analysis of survey data |
| topic | Astronomy Data analysis Data mining COMPUTERS / General bisacsh SCIENCE / Physics / Mathematical & Computational bisacsh Astronomy / Data processing fast Statistical astronomy fast Datenverarbeitung Astronomy Data processing Statistical astronomy Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Astronomie (DE-588)4003311-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Astronomy Data analysis Data mining COMPUTERS / General SCIENCE / Physics / Mathematical & Computational Astronomy / Data processing Statistical astronomy Datenverarbeitung Astronomy Data processing Datenanalyse Statistik Data Mining Statistische Schlussweise Astronomie Maschinelles Lernen |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=650266 |
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