Product Overview
From Follett
NOT AVAILABLE FOR SALE IN SOME COUNTRIES.;Title proper from title frame.;Mode of access: World Wide Web.;Includes bibliographical references and index.;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 efficiency -- 2.3 Seven types of computational Problems -- 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 -- References -- 4 Classical Statistical Inference -- 4.1 Classical vs. Bayesian Statistical Inference -- 4.2 Maximum Likelihood Estimation (MLE) -- 4.3 The goodness of Fit and Model Selection -- 4.4 ML Applied to Gaussian Mixtures: The Expectation Maximization Algorithm -- 4.5 Confidence estimates: the bootstrap and the jackknife -- 4.6 Hypothesis testing -- 4.7 Comparison of distributions -- 4.8 Nonparametric modeling and histograms -- 4.9 Selection effects and Luminosity Function Estimation -- 4.10 Summary -- References -- 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 -- 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 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 students and advanced undergraduates in physics and astronomy, and as an indespensable reference for researchers.