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Objective Bayesian nets for integrating consistent datasets

Landes, Jürgen, Williamson, Jon (2022) Objective Bayesian nets for integrating consistent datasets. Journal of Artificial Intelligence Research, 74 . pp. 393-458. ISSN 1076-9757. (doi:10.1613/jair.1.13363) (KAR id:95045)

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This paper addresses a data integration problem: given several mutually consistent datasets each of which measures a subset of the variables of interest, how can one construct a probabilistic model that fits the data and gives reasonable answers to questions which are under-determined by the data? Here we show how to obtain a Bayesian network model which represents the unique probability function that agrees with the probability distributions measured by the datasets and otherwise has maximum entropy. We provide a general algorithm, OBN-cDS, which offers substantial efficiency savings over the standard brute-force approach to determining the maximum entropy probability function. Furthermore, we develop modifications to the general algorithm which enable further efficiency savings but which are only applicable in particular situations. We show that there are circumstances in which one can obtain the model (i) directly from the data; (ii) by solving algebraic problems; and (iii) by solving relatively simple independent optimisation problems.

Item Type: Article
DOI/Identification number: 10.1613/jair.1.13363
Uncontrolled keywords: bayesian networks, knowledge representation, philosophical foundations, uncertainty
Subjects: B Philosophy. Psychology. Religion > BC Logic
Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Divisions: Divisions > Division of Arts and Humanities > Department of Philosophy
Depositing User: Jon Williamson
Date Deposited: 16 May 2022 13:21 UTC
Last Modified: 16 Jun 2022 08:50 UTC
Resource URI: (The current URI for this page, for reference purposes)
Williamson, Jon:
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