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Index
Cover
Title
Copyright
Contents
Preface
I Introduction
1 About the Book and Supporting Material
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 (1/4)
1.5 Description of Surveys and Data Sets Used in Examples (2/4)
1.5 Description of Surveys and Data Sets Used in Examples (3/4)
1.5 Description of Surveys and Data Sets Used in Examples (4/4)
1.6 Plotting and Visualizing the Data in This Book (1/2)
1.6 Plotting and Visualizing the Data in This Book (2/2)
1.7 How to Efficiently Use This Book
References
2 Fast Computation on Massive Data Sets
2.2 Analysis of Algorithmic Efficiency
2.3 Seven Types of Computational Problem
2.4 Seven Strategies for Speeding Things Up
2.5 Case Studies: Speedup Strategies in Practice (1/3)
2.5 Case Studies: Speedup Strategies in Practice (2/3)
2.5 Case Studies: Speedup Strategies in Practice (3/3)
References
II Statistical Frameworks and Exploratory Data Analysis
3 Probability and Statistical Distributions
3.1 Brief Overview of Probability and Random Variables (1/2)
3.1 Brief Overview of Probability and Random Variables (2/2)
3.2 Descriptive Statistics (1/2)
3.2 Descriptive Statistics (2/2)
3.3 Common Univariate Distribution Functions (1/4)
3.3 Common Univariate Distribution Functions (2/4)
3.3 Common Univariate Distribution Functions (3/4)
3.3 Common Univariate Distribution Functions (4/4)
3.4 The Central Limit Theorem
3.5 Bivariate and Multivariate Distribution Functions (1/2)
3.5 Bivariate and Multivariate Distribution Functions (2/2)
3.6 Correlation Coefficients
3.7 Random Number Generation for Arbitrary Distributions
References
4 Classical Statistical Inference
4.2 Maximum Likelihood Estimation (MLE) (1/2)
4.2 Maximum Likelihood Estimation (MLE) (2/2)
4.3 The goodness of Fit and Model Selection
4.4 ML Applied to Gaussian Mixtures: The Expectation Maximization Algorithm (1/2)
4.4 ML Applied to Gaussian Mixtures: The Expectation Maximization Algorithm (2/2)
4.5 Confidence Estimates: the Bootstrap and the Jackknife
4.6 Hypothesis Testing
4.7 Comparison of Distributions (1/3)
4.7 Comparison of Distributions (2/3)
4.7 Comparison of Distributions (3/3)
4.8 Nonparametric Modeling and Histograms
4.9 Selection Effects and Luminosity Function Estimation (1/2)
4.9 Selection Effects and Luminosity Function Estimation (2/2)
4.10 Summary
5 Bayesian Statistical Inference
5.1 Introduction to the Bayesian Method
5.2 Bayesian Priors
5.3 Bayesian Parameter Uncertainty Quantification
5.4 Bayesian Model Selection
5.5 Nonuniform Priors: Eddington, Malmquist, and Lutz–Kelker Biases
5.6 Simple Examples of Bayesian Analysis: Parameter Estimation (1/6)
5.6 Simple Examples of Bayesian Analysis: Parameter Estimation (2/6)
5.6 Simple Examples of Bayesian Analysis: Parameter Estimation (3/6)
5.6 Simple Examples of Bayesian Analysis: Parameter Estimation (4/6)
5.6 Simple Examples of Bayesian Analysis: Parameter Estimation (5/6)
5.6 Simple Examples of Bayesian Analysis: Parameter Estimation (6/6)
5.7 Simple Examples of Bayesian Analysis: Model Selection (1/2)
5.7 Simple Examples of Bayesian Analysis: Model Selection (2/2)
5.8 Numerical Methods for Complex Problems (MCMC) (1/2)
5.8 Numerical Methods for Complex Problems (MCMC) (2/2)
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 (1/2)
6.1 Nonparametric Density Estimation (2/2)
6.2 Nearest-Neighbor Density Estimation
6.3 Parametric Density Estimation (1/3)
6.3 Parametric Density Estimation (2/3)
6.3 Parametric Density Estimation (3/3)
6.4 Finding Clusters in Data (1/2)
6.4 Finding Clusters in Data (2/2)
6.5 Correlation Functions (1/3)
6.5 Correlation Functions (2/3)
6.5 Correlation Functions (3/3)
6.6 Which Density Estimation and Clustering Algorithms Should I Use?
References
7 Dimensionality and Its Reduction
7.2 The Data Sets Used in This Chapter
7.3 Principal Component Analysis (1/3)
7.3 Principal Component Analysis (2/3)
7.3 Principal Component Analysis (3/3)
7.4 Nonnegative Matrix Factorization
7.5 Manifold Learning (1/2)
7.5 Manifold Learning (2/2)
7.6 Independent Component Analysis and Projection Pursuit
7.7 Which Dimensionality Reduction Technique Should I Use?
References
8 Regression and Model Fitting
8.2 Regression for Linear Models (1/2)
8.2 Regression for Linear Models (2/2)
8.3 Regularization and Penalizing the Likelihood
8.4 Principal Component Regression
8.5 Kernel Regression
8.6 Locally Linear Regression
8.7 Nonlinear Regression
8.8 Uncertainties in the Data
8.9 Regression that is Robust to Outliers
8.10 Gaussian Process Regression
8.11 Overfitting, Underfitting, and Cross-Validation (1/2)
8.11 Overfitting, Underfitting, and Cross-Validation (2/2)
8.12 Which Regression Method Should I Use?
References
9 Classification
9.2 Assigning Categories: Classification
9.3 Generative Classification (1/2)
9.3 Generative Classification (2/2)
9.4 K-Nearest-Neighbor Classifier
9.5 Discriminative Classification
9.6 Support Vector Machines
9.7 Decision Trees (1/2)
9.7 Decision Trees (2/2)
9.8 Evaluating Classifiers: ROC Curves
9.9 Which Classifier Should I Use?
References
10 Time Series Analysis
10.1 Main Concepts for Time Series Analysis
10.2 Modeling Toolkit for Time Series Analysis (1/5)
10.2 Modeling Toolkit for Time Series Analysis (2/5)
10.2 Modeling Toolkit for Time Series Analysis (3/5)
10.2 Modeling Toolkit for Time Series Analysis (4/5)
10.2 Modeling Toolkit for Time Series Analysis (5/5)
10.3 Analysis of Periodic Time Series (1/6)
10.3 Analysis of Periodic Time Series (2/6)
10.3 Analysis of Periodic Time Series (3/6)
10.3 Analysis of Periodic Time Series (4/6)
10.3 Analysis of Periodic Time Series (5/6)
10.3 Analysis of Periodic Time Series (6/6)
10.4 Temporally Localized Signals
10.5 Analysis of Stochastic Processes (1/2)
10.5 Analysis of Stochastic Processes (2/2)
10.6 Which Method Should I Use for Time Series Analysis?
IV Appendices
A An Introduction to Scientific Computing with Python
A.2 The SciPy Universe
A.3 Getting Started with Python (1/3)
A.3 Getting Started with Python (2/3)
A.3 Getting Started with Python (3/3)
A.4 IPython: The Basics of Interactive Computing
A.5 Introduction to NumPy (1/2)
A.5 Introduction to NumPy (2/2)
A.6 Visualization with Matplotlib
A.7 Overview of Useful NumPy/SciPy Modules
A.8 Efficient Coding with Python and NumPy
A.9 Wrapping Existing Code in Python
A.10 Other Resources
B AstroML:Machine Learning for Astronomy
B.3 Tools Included in AstroML v0.1
C Astronomical Flux Measurements andMagnitudes
C.3 The Astronomical Magnitude Systems
D SQL Query for Downloading SDSS Data
E Approximating the Fourier Transform with the FFT
References
Visual Figure Index (1/2)
Visual Figure Index (2/2)
Index (1/2)
Index (2/2)
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