Duncan Foley – författare

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.

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.

This collection brings together significant new contributions to the Sraffa--based theories of production and distribution, from post-Keynesian arguments concerning monetary and macro economics to the history of thought and methodology. All of the authors are well established authorities in their field, and in this book they add stimulating and original pieces of analysis to the contemporary literature.Production, Distribution and Trade is divided into three parts. The first explores analytical issues in production and exchange theory, the second examines Postkeynesian Macroeconomics and the final part includes essays on the history of economic thought and methodology. This collection has been written in honour of Sergio Parrinello and is a fitting tribute to his untiring efforts to stimulate discussion among Classicists, Marxists, Postkeynesians, and Evolutionists.The book is a clear and convincing attempt to prove that an alternative paradigm to mainstream economics is alive and thriving and to argue that these perspectives shed better light on current economic problems, both as diagnosis and in terms of policy conclusions. The book will be of interest to Economics postgraduate students and researchers working in the Classical and Postkeynesian tradition.