Chapter

Why making Bayesian networks objectively Bayesian makes sense

Dawn E. Holmes

in Causality in the Sciences

Published in print March 2011 | ISBN: 9780199574131
Published online September 2011 | e-ISBN: 9780191728921 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199574131.003.0028
Why making Bayesian networks objectively Bayesian makes sense

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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

Chapter.  7619 words.  Illustrated.

Subjects: Logic

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