Objective Bayesian nets

Williamson, Jon (2005) Objective Bayesian nets. In: Artemov, Sergei and Barringer, Howard and Garcez, Artur d'Avila, eds. We Will Show Them: Essays in Honour of Dov Gabbay. College Publications, pp. 713-730. ISBN 9781904987260. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Abstract

I present a formalism that combines two methodologies: *objective Bayesianism* and *Bayesian nets*. According to *objective Bayesianism*, an agent's degrees of belief (i) ought to satisfy the axioms of probability, (ii) ought to satisfy constraints imposed by background knowledge, and (iii) should otherwise be as non-committal as possible (i.e. have maximum entropy). *Bayesian nets* offer an efficient way of representing and updating probability functions. An *objective Bayesian net* is a Bayesian net representation of the maximum entropy probability function. I show how objective Bayesian nets can be constructed, updated and combined, and how they can deal with cases in which the agent's background knowledge includes knowledge of qualitative *influence relationships*, e.g. causal influences. I then sketch a number of applications of the resulting formalism, showing how it can shed light on probability logic, causal modelling, logical reasoning, semantic reasoning, argumentation and recursive modelling.

Item Type: Book section
Subjects: B Philosophy. Psychology. Religion > B Philosophy (General)
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Faculties > Humanities > School of European Culture and Languages
Depositing User: Jon Williamson
Date Deposited: 10 Nov 2008 20:37
Last Modified: 13 Jun 2014 15:39
Resource URI: https://kar.kent.ac.uk/id/eprint/7369 (The current URI for this page, for reference purposes)
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