Objective Bayesian Nets from Consistent Datasets

Landes, Jürgen; Williamson, Jon (2016): Objective Bayesian Nets from Consistent Datasets. In: AIP Conference Proceedings, Vol. 1757, No. 1

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: Journal article
• Form of publication: Postprint
• Faculties: Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP); Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP) > Philosophy of Science; Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP) > Epistemology
• Subjects: 100 Philosophy and Psychology > 100 Philosophy; 100 Philosophy and Psychology > 120 Epistemology
• Language: English