Bayes for Beginners

About the Book

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.Bayes for Beginners is an entry-level book on Bayesian statistics. Writing in a casual, conversational tone, the authors walk the reader through many sample problems step-by-step, beginning with basic concepts in probability and ending with regression analysis with Markov Chain Monte Carlo (MCMC), Bayesian networks, and decision trees. The book is intended to provide readers with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems, setting the stage for continued learning.

Table of Contents:
Preface SECTION 1. BASICS OF PROBABILITY Chapter 1. Introduction to Probability Chapter 2. Joint, Marginal, and Conditional Probability SECTION 2. BAYES’ THEOREM AND BAYESIAN INFERENCE Chapter 3. Bayes’ Theorem Chapter 4. Bayesian Inference Chapter 5. The Author Problem: Bayesian Inference with Two Hypotheses Chapter 6. The Birthday Problem: Bayesian Inference with Multiple Hypotheses Chapter 7. The Portrait Problem: Bayesian Inference with Joint Likelihood SECTION 3. PROBABILITY DISTRIBUTIONS Chapter 8. Probability Mass Functions Chapter 9. Probability Density Functions SECTION 4. BAYESIAN CONJUGATES Chapter 10. The White House Problem: The Beta-Binomial Conjugate Chapter 11. The Shark Attack Problem: The Gamma-Poisson Conjugate Chapter 12. The Great Canadian Maple Syrup Heist: The Normal-Normal Conjugate SECTION 5. MCMC Chapter 13. The Shark Attack Problem with the Metropolis MCMC Chapter 14. MCMC Diagnostic Approaches Chapter 15. The White House Problem with the Metropolis-Hastings MCMC Chapter 16. The Great Maple Syrup Heist with Gibbs MCMC SECTION 6. [TITLE TO COME] Chapter 17. Simple Linear Regression with MCMC Chapter 18. Introduction to Bayesian Model Selection Chapter 19. Bayesian Belief Networks Chapter 20. Decision Trees APPENDICES Appendix 1. Beta-Binomial Conjugate Appendix 2. Gamma-Poisson Conjugate Appendix 3. Normal-Normal Conjugate Appendix 4. [Title to come] Appendix 5. [Title to come] Annotated Bibliography

About the Author :
Therese Donovan - Research Associate Professor, Assistant Unit Leader of VT Cooperative Fish & Wildlife Research Unit at The University of Vermont. Her areas of expertise include landscape ecology, wildlife population modeling and adaptive management. She received her Ph.D. in 1994 Biological Sciences, University of Missouri-Columbia.
Ruth Mickey -Professor Emerita at The University of Vermont. Her areas of expertise include categorical data analysis, logistic regression, research methods in epidemiology and multi-center studies. She received her Ph.D at the University of North Carolina.