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Chapter 1: Introduction and Data Sets
Chapter 2: Fast Computation and Massive Datasets
Chapter 3: Probability and Statistical Distributions
Overview of Probability and Random Variables
Descriptive Statistics
Univariate Distribution Functions
The Central Limit Theorem
Bivariate and multivariate distribution functions
Chapter 4: Classical Statistical Inference
Maximum Likelihood Estimation (MLE)
MLE applied to Gaussian mixtures
Confidence Estimates: The Bootstrap and The Jackknife
Hypothesis Testing
Comparison of distributions
Nonparametric modeling and selection effects
Chapter 5: Bayesian Statistical Inference
Parameter Estimation for a Gaussian Distribution
Parameter Estimation for a Binomial Distribution
Parameter estimation for the Cauchy (Lorentzian) distribution
Approximate Bayesian Computation Example
Hierarchical Bayes Example
Chapter 6: Searching for Structure in Point Data
Density Estimation for SDSS “Great Wall”
Searching for Structure in Point Data
Gaussian Mixture Models Example
Extreme Deconvolution
Chapter 7: Dimensionality and its Reduction
Dimensionality reduction
Chapter 8: Regression and Model Fitting
Measurement Errors in Linear Regression
Measurement errors in both dependent and independent variables
Chapter 9: Classification
Classification
Deep Learning: Classifying Astronomical Images
Chapter 10: Time Series Analysis
Modeling Toolkit For Time Series Analysis
Wavelets
Digital Filtering
Temporally localized signals
Analysis of Stochastic Processes
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