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, London, pp. 713-730. ISBN 978-1-904987-26-0. (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) (KAR id:7369)
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. |
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 |
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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: | Divisions > Division of Arts and Humanities > School of Culture and Languages |
Depositing User: | Jon Williamson |
Date Deposited: | 10 Nov 2008 20:37 UTC |
Last Modified: | 16 Nov 2021 09:45 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/7369 (The current URI for this page, for reference purposes) |
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