Making Statistics Work

Information Theory and Bayesian Inference

AvDuncan Foley,Ellis Scharfenaker

Häftad, Engelska, 2026

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2026-07-14
Mått:156 x 235 x 18 mm
Vikt:472 g
Format:Häftad
Språk:Engelska
Antal sidor:320
Förlag:Columbia University Press
ISBN:9780231222044

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Mer om författaren

Duncan K. Foley is the Leo Model Professor Emeritus of Economics at the New School for Social Research. He is the author of Understanding Capital: Marx’s Economic Theory (1986) and Adam’s Fallacy: A Guide to Economic Theology (2006) and coauthor of Growth and Distribution (second edition, 2019), among other books.Ellis Scharfenaker is an associate professor of economics at the University of Utah. His research integrates Bayesian inference, information theory, and political economy to study industrial dynamics and income distribution.

Recensioner i media

At last, a statistics book that engages the philosophical foundations of probability and fully develops their implications through to practice. Clear, rigorous, and refreshingly honest about assumptions, it sets a new standard and should be required reading for anyone serious about statistics.

Innehållsförteckning

Part I. Basic Concepts1. The Statistical Problem2. Probability3. Probabilities and Information4. Likelihoods and Data5. Priors and Constraints6. Organizing Statistical InferencePart II. Multinomial Models7. The Binomial Model for Repeated Bernoulli Trials8. The Multinomial Model9. The Quantal Response Model10. Fourier Transforms and Time Series11. Complex Amplitudes as Multinomial ParametersPart III. Real Number Observations12. Real Scalar Statistics13. Real Vector StatisticsPart IV. Advanced Topics14. Constrained Maximum Entropy15. Confounding Variables and Limited InformationPart V. Philosophical and Methodological Puzzles16. De Finetti’s Economic Model of Probability17. The Frequency Model of Probability18. Mathematical AppendixNotesBibliographyAuthor IndexSubject Index