.png){kind=icon}
Return
Computing Sobol indices in probabilistic graphical models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ress.2022.108573.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We show how to apply Sobol???s method of global sensitivity analysis to measure the influence exerted by a set of nodes??? evidence on a quantity of interest expressed by a Bayesian network. Our method exploits the network structure so as to transform the problem of Sobol index estimation into that of marginalization inference and, unlike Monte Carlo based estimators for variance-based sensitivity analysis, it gives exact results when exact inference is used. Moreover, the method supports the case of correlated inputs and it is efficient as long as eliminating the inputs??? ancestors is computationally affordable. The proposed algorithms are inspired by the field of tensor networks and generalize earlier tensor sensitivity techniques from the acyclic to the cyclic case. We demonstrate our method on three medium to large Bayesian networks in the areas of structural reliability and project risk management.
Keywords:
Global sensitivity analysis
Bayesian networks
Sobol indices
Uncertainty quantification
Tensor networks
Journal
R
IF:
11
Papers:
9.0K
Citations:
4.2W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more