David Corfield and Jon Williamson eds

Kluwer Applied Logic Series 2001

### Contents

Editorial Foreword.

Editorial Preface.

**Introduction**: Bayesianism into the 21st Century; J. Williamson, D. Corfield.

**Bayesianism, Causality and Networks.**

Bayesianism and Causality, or, Why I am only a Half-Bayesian; J. Pearl.

Causal Inference without Counterfactuals; P. Dawid.

Foundations for Bayesian Networks; J. Williamson.

Probabilistic Learning Models; P. Williams.

**Logic, Mathematics and Bayesianism.**

The Logic of Bayesian Probability; C. Howson.

Subjectivism, Objectivism and Objectivity in Bruno de Finetti’s Bayesianism;

M.C. Galavotti.

Bayesianism in Mathematics; D. Corfield.

Common Sense and Stochastic Independence; J. Paris, A. Vencovski.

Integrating Probabilistic and Logical Reasoning; J. Cussens.

**Bayesianism and Decision Theory.**

Ramsey and the Measurement of Belief; R. Bradley.

Bayesianism and Independence; E.F. McClennen.

The Paradox of the Bayesian Experts; P. Mongin.

**Criticisms of Bayesianism.**

Bayesian Learning and Expectations Formation: Anything Goes; M. Albert.

Bayesianism and the Fixity of the Theoretical Framework; D. Gillies.

Principles of Inference and their Consequences; D. Mayo, M. Kruse.

Index.

### Reviews

I’d be very grateful if you can email me if you spot any reviews that are not mentioned here – Jon Williamson.

Philosophy of Science 70(1) – Mathias Risse: “Corfield and Williamsonâ€™s impressive volume is recommended reading for anybody trying to stay abreast of Bayesian research. Bayesianism has moved with full force into the 21st century. Hopefully, philosophy departments will not be left behind.”

British Journal for the Philosophy of Science 54(3) – Erik J. Olsson: “This is an excellent and substantial (400 pages) collection of papers by some of the leading researchers in the field. … It is unusually coherent and focused, and yet manages to cover a whole range of topicsâ€”from philosophical issues concerning the interpretation of probability to more technical aspects of Bayesian statistics. What I find particularly valuable is that more than one voice is given a hearing on many particular issues, such as causality and the logical interpretation of probability. Anyone with an interest in Bayesianism who is not afraid of a formula or two will find this book to be of great value.”