How to model mutually exclusive events based on independent causal pathways in Bayesian network models

Date

2016-12-01

Author

Fenton, Norman
Neil, Martin
Lagnado, David
Marsh, William
Yet, Barbaros
Constantinou, Anthony

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

189
views

0
downloads

We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed 'solution', which introduces a simple constraint node that enforces mutual exclusivity, fails to preserve the prior probabilities of the events, while other proposed solutions involve major changes to the original model. We provide a novel and simple solution to this problem that works in all cases where the mutually exclusive nodes have no common ancestors. Our solution uses a special type of constraint and auxiliary node together with formulas for assigning their necessary conditional probability table values. The solution enforces mutual exclusivity between events and preserves their prior probabilities while leaving all original BN nodes unchanged. (C) 2016 The Authors. Published by Elsevier B.V.

Subject Keywords

Bayesian networks, Mutually exclusive events, Causes, Uncertain reasoning

URI

https://hdl.handle.net/11511/56213

Journal

KNOWLEDGE-BASED SYSTEMS

DOI

https://doi.org/10.1016/j.knosys.2016.09.012

Collections

Graduate School of Informatics, Article

Suggestions

OpenMETU
Core

On the use of complex stretching coordinates in generalized finite difference method with applications in inhomogeneous visco-elasto dynamics Korkut, Fuat; Mengi, Yalcin; Tokdemir, Turgut (2022-01-01) In the study, in conjunction with perfectly matched layer (PML) analysis, an approach is proposed for the evaluation of complex derivatives directly in terms of complex stretching coordinates of points in PML. For doing this within the framework of generalized finite difference method (GFDM), a difference equation is formulated and presented, where both the function values and coordinates of data points might be complex. The use of the proposed approach is considered in the analysis of inhomogeneous visco-e...
Shortcuts to high symmetry solutions in gravitational theories Deser, S; Tekin, Bayram (IOP Publishing, 2003-11-21) We apply the Weyl method, as sanctioned by Palais' symmetric criticality theorems, to obtain those-highly symmetric-geometries amenable to explicit solution, in generic gravitational models and dimension. The technique consists of judiciously violating the rules of variational principles by inserting highly symmetric, and seemingly gauge fixed, metrics into the action, then varying it directly to arrive at a small number of transparent, indexless, field equations. Illustrations include spherically and axial...
On endomorphisms of surface mapping class groups Korkmaz, Mustafa (Elsevier BV, 2001-05-01) In this paper, we prove that every endomorphism of the mapping class group of an orientable surface onto a subgroup of finite index is in fact an automorphism.
Comparison of two inference approaches in Gaussian graphical models Purutçuoğlu Gazi, Vilda; Wit, Ernst (Walter de Gruyter GmbH, 2017-04-01) Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.
Gaussian graphical approaches in estimation of biological systems Ayyıldız, Ezgi; Purutçuoğlu Gazi, Vilda; Department of Statistics (2013) The Gaussian Graphical Model (GGM) is one of the well-known deterministic inference methods which is based on the conditional independency of nodes in the system. In this study we consider to implement this approach in small and relatively large networks under different singularity and sparsity conditions. In inference of these systems we perform lasso and L-1 penalized lasso regression approaches and select the best ﬁtted model to the data by using different criteria. Among many alternatives, we apply the ...

Citation Formats

N. Fenton, M. Neil, D. Lagnado, W. Marsh, B. Yet, and A. Constantinou, “How to model mutually exclusive events based on independent causal pathways in Bayesian network models,” KNOWLEDGE-BASED SYSTEMS, pp. 39–50, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56213.