Objective bayesian nets


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.



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Inductive influence.Jon Williamson - 2007 - British Journal for the Philosophy of Science 58 (4):689 - 708.
In Defence of Objective Bayesianism.Jon Williamson - 2010 - Oxford University Press.


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Jon Williamson
University of Kent

Citations of this work

Probabilistic Logics and Probabilistic Networks.Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler & Jon Williamson - 2010 - Dordrecht, Netherland: Synthese Library. Edited by Gregory Wheeler, Rolf Haenni, Jan-Willem Romeijn & and Jon Williamson.
Objective Bayesian nets for integrating consistent datasets.Jürgen Landes & Jon Williamson - 2022 - Journal of Artificial Intelligence Research 74:393-458.
Inductive influence.Jon Williamson - 2007 - British Journal for the Philosophy of Science 58 (4):689 - 708.
Non-additive degrees of belief.Rolf Haenni - 2009 - In Franz Huber & Christoph Schmidt-Petri (eds.), Degrees of Belief. Springer. pp. 121--159.
Models in Systems Medicine.Jon Williamson - 2017 - Disputatio 9 (47):429-469.

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