Objective Bayesian nets from consistent datasets

Landes, Jürgen, Williamson, Jon (2016) Objective Bayesian nets from consistent datasets. In: BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 35TH INTERNATIONAL WORKSHOP ON BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. 1757. 020007. AIP ISBN 978-0-7354-1415-0. (doi:10.1063/1.4959048) (KAR id:56779)

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Official URL: http://doi.org/10.1063/1.4959048

Abstract

This paper addresses the problem of finding a Bayesian net representation of the probability function that agrees with the distributions of multiple consistent datasets and otherwise has maximum entropy. We give a general algorithm which is significantly more efficient than the standard brute-force approach. Furthermore, we show that in a wide range of cases such a Bayesian net can be obtained without solving any optimisation problem.

Item Type:	Conference or workshop item (Paper)
DOI/Identification number:	10.1063/1.4959048
Subjects:	B Philosophy. Psychology. Religion > BC Logic Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Divisions:	Divisions > Division of Arts and Humanities > School of Culture and Languages
Depositing User:	Jon Williamson
Date Deposited:	10 Aug 2016 08:42 UTC
Last Modified:	05 Nov 2024 10:46 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/56779 (The current URI for this page, for reference purposes)

University of Kent Author Information

Williamson, Jon.

Creator's ORCID:	https://orcid.org/0000-0003-0514-4209
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