David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Philosophia Mathematica 15 (3):389-396 (2007)
Bayesian networks are computer programs which represent probabilitistic relationships graphically as directed acyclic graphs, and which can use those graphs to reason probabilistically , 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 :This development, together with a parallel related growth in the use of causal discovery algorithms which automate the learning of Bayesian networks from sample data, has generated considerable interest, and controversy, within the philosophy-of-science community.Three central questions bringing together AI researchers and philosophers of science are: Are Bayesian networks Bayesian? What is the relation between probability and causality? Are the assumptions behind causal discovery of Bayesian networks realistic or fantastical?Jon Williamson, as a philosopher of science with a keen interest in the technology, asks and answers these questions in his new book. Although it is self-contained, his book is not very likely as an introduction to the technology , nor is it optimal even as an introduction to the philosophical problems in interpreting Bayesian networks . Rather Williamson's book is an attempt to move the debate forward by solving the central problems of the …
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Jon Williamson (2006). Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. Journal of Logic, Language and Information 15 (1-2):155-178.
S. Choi (2006). Review: Bayesian Nets and Causality: Philosophical and Computational Foundations. [REVIEW] Mind 115 (458):502-506.
Clark Glymour (2009). Jon Williamson Bayesian Nets and Causality. British Journal for the Philosophy of Science 60 (4):849-855.
Darrell P. Rowbottom (2008). On the Proximity of the Logical and 'Objective Bayesian' Interpretations of Probability. Erkenntnis 69 (3):335-349.
Jon Williamson (2001). Foundations for Bayesian Networks. In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers 75--115.
Mathias Risse (2003). Bayesianism, —Quo Vadis?—Critical Notice: David Corfield and Jon Williamson (Eds.), Foundations of Bayesianism. Philosophy of Science 70 (1):225-231.
Bradford McCall (2008). Jon Williamson, Bayesian Nets and Causality: Philosophical and Computational Foundations. [REVIEW] Minds and Machines 18 (2):301-302.
Added to index2009-01-28
Total downloads11 ( #250,455 of 1,779,270 )
Recent downloads (6 months)1 ( #291,352 of 1,779,270 )
How can I increase my downloads?