Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today.
Some of these papers seek to clarify the relationships between Bayesian, causal and logical reasoning. Others consider the application of Bayesianism to artificial intelligence, decision theory, statistics and the philosophy of science and mathematics. The volume includes important criticisms of Bayesian reasoning and also gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. The upshot is a plethora of new problems and directions for Bayesians to pursue.
The book will be of interest to graduate students or researchers who wish to learn more about Bayesianism than can be provided by introductory textbooks to the subject. Those involved with the applications of Bayesian reasoning will find essential discussion on the validity of Bayesianism and its limits, while philosophers and others interested in pure reasoning will find new ideas on normativity and the logic of belief.
Author(s): Jon Williamson, David Corfield (auth.), David Corfield, Jon Williamson (eds.)
Series: Applied Logic Series 24
Edition: 1
Publisher: Springer Netherlands
Year: 2001
Language: English
Pages: 416
Tags: Philosophy of Science; Artificial Intelligence (incl. Robotics); Probability Theory and Stochastic Processes; Statistics, general; Microeconomics
Front Matter....Pages i-xiii
Introduction: Bayesianism into the 21st Century....Pages 1-16
Front Matter....Pages 17-17
Bayesianism and Causality, or, Why I am Only a Half-Bayesian....Pages 19-36
Causal Inference without Counterfactuals....Pages 37-74
Foundations for Bayesian Networks....Pages 75-115
Probabilistic Learning Models....Pages 117-134
Front Matter....Pages 135-135
The Logic of Bayesian Probability....Pages 137-159
Subjectivism, Objectivism and Objectivity in Bruno de Finetti’s Bayesianism....Pages 161-174
Bayesianism in Mathematics....Pages 175-201
Common Sense and Stochastic Independence....Pages 203-240
Integrating Probabilistic and Logical Reasoning....Pages 241-260
Front Matter....Pages 261-261
Ramsey and the Measurement of Belief....Pages 263-290
Bayesianism and Independence....Pages 291-307
The Paradox of the Bayesian Experts....Pages 309-338
Front Matter....Pages 339-339
Bayesian Learning and Expectations Formation: Anything Goes....Pages 341-362
Bayesianism and the Fixity of the Theoretical Framework....Pages 363-379
Principles of Inference and Their Consequences....Pages 381-403
Back Matter....Pages 405-416
