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Causality in the Sciences$
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Phyllis McKay Illari, Federica Russo, and Jon Williamson

Print publication date: 2011

Print ISBN-13: 9780199574131

Published to Oxford Scholarship Online: September 2011

DOI: 10.1093/acprof:oso/9780199574131.001.0001

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Why making Bayesian networks objectively Bayesian makes sense

Why making Bayesian networks objectively Bayesian makes sense

Chapter:
(p.583) 28 Why making Bayesian networks objectively Bayesian makes sense
Source:
Causality in the Sciences
Author(s):

Dawn E. Holmes

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199574131.003.0028

It is well‐known that Bayesian networks are so‐called because of their use of Bayes theorem for probabilistic inference. However, since Bayesian networks commonly use frequentist probabilities exclusively, is this sense they are not Bayesian. In this chapter it is argued that Bayesian networks that are objectively Bayesian, in other words those whose prior distribution is based on all and only the available information, have certain desirable properties and strengths over and above those based solely on the frequentist approach to probability. It is demonstrated, through an example, that these specially constructed graphical models may be used in otherwise intractable situations where data is unavailable or scarce and decisions need to be made.

Keywords:   probability, Bayesian networks, maximum entropy, Bayesianism

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