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Foundations of Bayesianism
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Editor(s):
David Corfield
,
Jon Williamson
Publication date
(Print):
2001
Publisher:
Springer Netherlands
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ISBN (Print):
978-90-481-5920-8
ISBN (Electronic):
978-94-017-1586-7
Publication date (Print):
2001
DOI:
10.1007/978-94-017-1586-7
SO-VID:
bbc0e13f-1b99-4b5d-a797-c80ecaa7c044
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http://www.springer.com/tdm
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Book chapters
pp. 1
Introduction: Bayesianism into the 21st Century
pp. 19
Bayesianism and Causality, or, Why I am Only a Half-Bayesian
pp. 37
Causal Inference without Counterfactuals
pp. 75
Foundations for Bayesian Networks
pp. 117
Probabilistic Learning Models
pp. 137
The Logic of Bayesian Probability
pp. 161
Subjectivism, Objectivism and Objectivity in Bruno de Finetti’s Bayesianism
pp. 175
Bayesianism in Mathematics
pp. 203
Common Sense and Stochastic Independence
pp. 241
Integrating Probabilistic and Logical Reasoning
pp. 263
Ramsey and the Measurement of Belief
pp. 291
Bayesianism and Independence
pp. 309
The Paradox of the Bayesian Experts
pp. 341
Bayesian Learning and Expectations Formation: Anything Goes
pp. 363
Bayesianism and the Fixity of the Theoretical Framework
pp. 381
Principles of Inference and Their Consequences
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