Link strength in Bayesian networks

This thesis introduces the concept of a connection strength (CS) between two nodes of a propositional Bayesian network (BN). Connection strength is a generalization of node independence, from a binary property to a graded measure. The connection strength from node A to node B is a measure of the max...

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Main Author: Boerlage, Brent
Format: Others
Language:English
Published: 2008
Online Access:http://hdl.handle.net/2429/2041
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spelling ndltd-UBC-oai-circle.library.ubc.ca-2429-20412018-01-05T17:30:58Z Link strength in Bayesian networks Boerlage, Brent This thesis introduces the concept of a connection strength (CS) between two nodes of a propositional Bayesian network (BN). Connection strength is a generalization of node independence, from a binary property to a graded measure. The connection strength from node A to node B is a measure of the maximum amount that the belief in B will change when the truth value of A is learned. If the belief in B does not change, they are independent, and if it changes a great deal, they are strongly connected. It also introduces the link strength (LS) between two adjacent nodes, which is an upper bound on that part of the connection strength between them which is due only to the link between them (and not other paths which may connect them). Calculating connection strengths is computationally expensive, while calculating link strengths is not. An algorithm is provided which finds a bound on the connection strength between any two nodes by combining link strengths along the paths connecting them (which is of complexity linear in the number of links). Such an algorithm lends substance to notions of an "effect" flowing along paths, and "effect" being attenuated by "weak" links, which is terminology that has appeared often in the literature, but only as an intuitive idea. An algorithm for faster, approximate BN inference is presented, and connection strengths are used to provide bounds for its error. A system is proposed for BN diagrams to be drawn with strong links represented by heavy lines and weak links by fine lines, as a visualization aid for humans. Another visualization aid which is explored is the iso-CS contour map, in which the amount that one particular node can effect each of the other nodes is shown as contour lines super-imposed on a regular BN diagram. Anon-trivial example BN is presented, some of its connection strengths are calculated, CS contour maps are constructed for it, and it is displayed with link strength indicated by line width. Science, Faculty of Computer Science, Department of Graduate 2008-09-16T17:17:07Z 2008-09-16T17:17:07Z 1992 1992-11 Text Thesis/Dissertation http://hdl.handle.net/2429/2041 eng For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. 4149050 bytes application/pdf
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description This thesis introduces the concept of a connection strength (CS) between two nodes of a propositional Bayesian network (BN). Connection strength is a generalization of node independence, from a binary property to a graded measure. The connection strength from node A to node B is a measure of the maximum amount that the belief in B will change when the truth value of A is learned. If the belief in B does not change, they are independent, and if it changes a great deal, they are strongly connected. It also introduces the link strength (LS) between two adjacent nodes, which is an upper bound on that part of the connection strength between them which is due only to the link between them (and not other paths which may connect them). Calculating connection strengths is computationally expensive, while calculating link strengths is not. An algorithm is provided which finds a bound on the connection strength between any two nodes by combining link strengths along the paths connecting them (which is of complexity linear in the number of links). Such an algorithm lends substance to notions of an "effect" flowing along paths, and "effect" being attenuated by "weak" links, which is terminology that has appeared often in the literature, but only as an intuitive idea. An algorithm for faster, approximate BN inference is presented, and connection strengths are used to provide bounds for its error. A system is proposed for BN diagrams to be drawn with strong links represented by heavy lines and weak links by fine lines, as a visualization aid for humans. Another visualization aid which is explored is the iso-CS contour map, in which the amount that one particular node can effect each of the other nodes is shown as contour lines super-imposed on a regular BN diagram. Anon-trivial example BN is presented, some of its connection strengths are calculated, CS contour maps are constructed for it, and it is displayed with link strength indicated by line width. === Science, Faculty of === Computer Science, Department of === Graduate
author Boerlage, Brent
spellingShingle Boerlage, Brent
Link strength in Bayesian networks
author_facet Boerlage, Brent
author_sort Boerlage, Brent
title Link strength in Bayesian networks
title_short Link strength in Bayesian networks
title_full Link strength in Bayesian networks
title_fullStr Link strength in Bayesian networks
title_full_unstemmed Link strength in Bayesian networks
title_sort link strength in bayesian networks
publishDate 2008
url http://hdl.handle.net/2429/2041
work_keys_str_mv AT boerlagebrent linkstrengthinbayesiannetworks
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