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Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journ ... More

*Keywords: *
probability,
conditional probability,
Bayes’ Theorem,
scientific method,
prior distribution,
posterior distribution,
likelihood,
conjugate prior,
Markov Chain Monte Carlo,
Bayesian belief network,
decision tree

Print publication date: 2019 | Print ISBN-13: 9780198841296 |

Published to Oxford Scholarship Online: July 2019 | DOI:10.1093/oso/9780198841296.001.0001 |

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## Front Matter

## End Matter

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Appendix 1 The Beta-Binomial Conjugate Solution

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Appendix 2 The Gamma-Poisson Conjugate Solution

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Appendix 3 The Normal-Normal Conjugate Solution

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Appendix 4 Conjugate Solutions for Simple Linear Regression

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Appendix 5 The Standardization of Regression Data

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Bibliography

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Hyperlinks Accessed August 2017

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Name Index

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Subject Index

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