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.

Objective Bayesian Nets From Consistent Datasets / J. Landes, J. Williamson (AIP CONFERENCE PROCEEDINGS). - In: Bayesian inference and maximum entropy methods in science and engineering / [a cura di] A. Giffin, K.H. Knuth. - [s.l] : AIP, 2016. - ISBN 978-0-7354-1415-0. - pp. 1-8 (( Intervento presentato al 35. convegno International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt) tenutosi a Potsdam nel 2015 [10.1063/1.4959048].

Objective Bayesian Nets From Consistent Datasets

J. Landes;
2016

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/948696
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