Bayesian Importance Measures for Network Edges Under Saturated Lagrangian Poisson Failures

Volume: 70, Issue: 1, Pages: 110 - 120
Published: Mar 1, 2021
Abstract
Bayesian importance measures (BIMs) are useful tools for quantifying the contribution of an edge to the up or down state of the network. This article investigates BIMs for the K-terminal networks under the assumption that the failures of edges occur according to a branching process in which the total number of the failed edges follows a saturated Lagrangian Poisson distribution (SLPD). First, we derive two types of BIM equations when the total...
Paper Details
Title
Bayesian Importance Measures for Network Edges Under Saturated Lagrangian Poisson Failures
Published Date
Mar 1, 2021
Volume
70
Issue
1
Pages
110 - 120
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