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This is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. The volume includes important criticisms of Bayesian reasoning and gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. It will be of interest to graduate students, researchers, those involved with the applications of Bayesian reasoning, and philosophers.

Foundations of Bayesianism

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. Vencovská. 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.