Skip Navigation


Philosophia Mathematica Advance Access originally published online on June 20, 2007
Philosophia Mathematica 2007 15(3):389-396; doi:10.1093/philmat/nkm024
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
15/3/389    most recent
nkm024v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Korb, K. B.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Copyright © The Author 2007. Published by Oxford University Press.

Book Review

JON WILLIAMSON. Bayesian Nets and Causality: Philosophical and Computational Foundations

Kevin B. Korb*

* Clayton School of Information Technology, Monash University, Clayton, Victoria, Australia

Correspondence: kevin.korb@infotech.monash.edu.au

Jon Williamson. Bayesian Nets and Causality: Philosophical and Computational Foundations. Oxford: Oxford University Press, 2005. ISBN 0-19-853079-X (cloth). Pp. ix + 239

The first 150 words of the full text of this article appear below.

Bayesian networks are computer programs which represent probabilitistic relationships graphically as directed acyclic graphs, and which can use those graphs to reason probabilistically (as in Bayesian updating), often at relatively low computational cost. Almost every expert system in the past tried to support probabilistic reasoning, but because of the computational difficulties they took approximating short-cuts, such as those afforded by MYCIN's certainty factors. That all changed with the publication of Judea Pearl's Probabilistic Reasoning in Intelligent Systems, in 1988, which synthesized a decade of research making accurate graphical probabilistic reasoning computationally achievable.

Bayesian network technology is now one of the fastest growing fields of research in artificial intelligence. That it has become a publication industry in its own right is shown by a search on Google scholar (simply for publications using the keyword ‘Bayesian network’, restricted to the years indicated):

This development, together with a parallel related growth in . . . [Full Text of this Article]


   1. Are Bayesian Networks Bayesian?
 

   2. What Is the Relation between Probability and Causality?
 

   3. Are the Assumptions Behind Causal Discovery Fantastical?
 

   Conclusion
 

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?