Wow! eBook
Making Statistics Work: Information Theory and Bayesian Inference
Author(s): Duncan Foley (Author), Ellis Scharfenaker (Author)
- Publisher: Columbia University Press
- Publication Date: July 14, 2026
- Language: English
- Print length: 320 pages
- ISBN-10: 0231222041
- ISBN-13: 9780231222044
Book Description
Editorial Reviews
Review
“In
Making Statistics Work, Duncan K. Foley and Ellis Scharfenaker combine information theory, Bayesian updating, and probability theory into a single logical framework for statistical inference under imperfect and insufficient information. The authors provide many examples, making the book very accessible. This is a valuable resource for scientists, students, and teachers across disciplines.” — Amos Golan, American University and the Santa Fe Institute“
Making Statistics Work introduces a robust framework that brings together information theory and Bayesian inference through entropy-maximizing priors. Offering both readability and rigor, this book is a refreshing alternative to the conventional statistical education.” — Jangho Yang, University of WaterlooAbout the Author
{“@context”:”https://schema.org”,”@type”:”Book”,”name”:”Making Statistics Work: Information Theory and Bayesian Inference”,”image”:”https://m.media-amazon.com/images/I/51eoEVTGh9L._SY445_SX342_FMwebp_.jpg”,”author”:{“@type”:”Person”,”name”:”Duncan Foley (Author), Ellis Scharfenaker (Author)”},”publisher”:{“@type”:”Organization”,”name”:”Columbia University Press”},”datePublished”:”July 14, 2026″,”isbn”:”9780231222044″,”numberOfPages”:320,”inLanguage”:”English”,”description”:”Conventional “frequentist” methods that dominate the field of statistics are generally inconsistent and liable to catastrophic failure in some contexts. These weaknesses have become particularly concerning in relation to crises of replicability and credibility in science. Two alternatives have been proposed to address these flaws―classical Bayesian inference and the principle of maximum entropy―but the connections between them remain controversial.Making Statistics Work presents a synthesis of information theory and Bayesian inference that addresses these fundamental problems. It provides a consistent, powerful, and flexible framework for data inference based on rigorous logic derived from first principles, allowing for new approaches to many of the unresolved questions of statistics. Duncan K. Foley and Ellis Scharfenaker illustrate the application of this framework and the reasoning behind it across a variety of important statistical problems, such as the inference underlying “gold standard” clinical trials, models of human behavior employed in behavioral finance and psychology, analysis of macroeconomic policy, the relationship of classical probability to quantum physics, and the limitations of linear regression analysis. Making Statistics Work offers new insight into contentious topics, from problems of causality and confounding variables in randomized experimental trials to the foundations of Bayesian and frequentist probability theory.”,”url”:”https://www.amazon.com/dp/0231222041/”,”bookFormat”:”http://schema.org/EBook”,”additionalType”:”http://schema.org/PDF”,”fileSize”:”41 MB”,”accessibilityFeature”:[“login required”,”member access only”],”accessibilitySummary”:”PDF version available to authenticated members only. File size: 41 MB.”}
