Objective Bayesian Nets from Consistent Datasets

Landes, Jürgen und Williamson, Jon (2016): Objective Bayesian Nets from Consistent Datasets. In: AIP Conference Proceedings, Bd. 1757, Nr. 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.

• Dokumententyp: Zeitschriftenartikel
• Publikationsform: Postprint
• Fakultät: Philosophie, Wissenschaftstheorie und Religionswissenschaft > Munich Center for Mathematical Philosophy (MCMP) ; Philosophie, Wissenschaftstheorie und Religionswissenschaft > Munich Center for Mathematical Philosophy (MCMP) > Philosophy of Science ; Philosophie, Wissenschaftstheorie und Religionswissenschaft > Munich Center for Mathematical Philosophy (MCMP) > Epistemology
• Themengebiete: 100 Philosophie und Psychologie > 100 Philosophie ; 100 Philosophie und Psychologie > 120 Epistemologie
• Sprache: Englisch