Table of Contents

*Frontmatter, pg. i*Contents, pg. v*Preface, pg. ix*1. About the Book and Supporting Material, pg. 3*2. Fast Computation on Massive Data Sets, pg. 43*3. Probability and Statistical Distributions, pg. 69*4. Classical Statistical Inference, pg. 123*5. Bayesian Statistical Inference, pg. 175*6. Searching for Structure in Point Data, pg. 249*7. Dimensionality and Its Reduction, pg. 289*8. Regression and Model Fitting, pg. 321*9. Classification, pg. 365*10. Time Series Analysis, pg. 403*A. An Introduction to Scientific Computing with Python, pg. 471*B. AstroML: Machine Learning for Astronomy, pg. 511*C. Astronomical Flux Measurements and Magnitudes, pg. 515*D. SQL Query for Downloading SDSS Data, pg. 519*E. Approximating the Fourier Transform with the FFT, pg. 521*Visual Figure Index, pg. 527*Index, pg. 533

About the Author

?eljko Ivezic is professor of astronomy at the University of Washington. Andrew J. Connolly is professor of astronomy at the University of Washington. Jacob T. VanderPlas is an NSF postdoctoral research fellow in astronomy and computer science at the University of Washington. Alexander Gray is professor of computer science at Georgia Institute of Technology.

Reviews

Winner of the 2016 IAA Outstanding Publication Award, International Astrostatistics Association "Ivezic and colleagues at the University of Washington and the Georgia Institute of Technology have written a comprehensive, accessible, well-thought-out introduction to the new and burgeoning field of astrostatistics... The authors provide another valuable service by discussing how to access data from key astronomical research programs."--Choice "A substantial work that can be of value to students and scientists interesting in mining the vast amount of astronomical data collected to date... A well-prepared introduction to this material... If data mining and machine learning fall within your interest area, this text deserves a place on your shelf."--International Planetarium Society