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Statistics, Data Mining, and Machine Learning in AstronomyA Practical Python Guide for the Analysis of Survey Data$
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Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Željko Ivezic, Andrew J. Connolly, Jacob T VanderPlas, and Alexander Gray

Print publication date: 2014

Print ISBN-13: 9780691151687

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691151687.001.0001

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PRINTED FROM PRINCETON SCHOLARSHIP ONLINE (www.princeton.universitypressscholarship.com). (c) Copyright Princeton University Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in PRSO for personal use.date: 26 May 2022

Probability and Statistical Distributions

Probability and Statistical Distributions

Chapter:
(p.69) 3 Probability and Statistical Distributions
Source:
Statistics, Data Mining, and Machine Learning in Astronomy
Author(s):

Željko Ivezi

Andrew J. Connolly

Jacob T. VanderPlas

Alexander Gray

Željko Ivezi

Andrew J. Connolly

Jacob T. VanderPlas

Alexander Gray

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691151687.003.0003

This chapter reviews notation and basic concepts in probability and statistics. The coverage of various topics cannot be complete, and it is aimed at concepts needed to understand material covered in the book. For an in-depth discussion of probability and statistics, the chapter suggests that readers please refer to numerous readily available textbooks, such as Bar89, Lup93, WJ03, Wass10, mentioned in this book. The chapter begins with a brief overview of probability and random variables. It then reviews the most common univariate and multivariate distribution functions, and correlation coefficients. It also summarizes the central limit theorem and discusses how to generate mock samples (random number generation) for a given distribution function.

Keywords:   notations, random variables, univariate distribution, multivariate distribution functions, correlation coefficients, central limit theorem, probability, statistics

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