Global sensitivity analysis of uncertain parameters in Bayesian networks
Traditionally, the sensitivity analysis of a Bayesian network studies the impact of individually modifying the entries of its conditional probability tables in a one-at-a-time (OAT) fashion. However, this approach fails to give a comprehensive account of each inputs' relevance, since simultaneo...
| Authors: | , |
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| Format: | article |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | IE |
| Repository: | Repositorio IE |
| OAI Identifier: | oai:repositorio.ie.edu:20.500.14417/3904 |
| Online Access: | https://doi.org/10.1016/j.ijar.2025.109368 https://hdl.handle.net/20.500.14417/3904 https://www.sciencedirect.com/science/article/abs/pii/S0888613X2500009X |
| Access Level: | Embargoed access |
| Keyword: | 33 Ciencias Tecnológicas ODS 9 - Industria, innovación e infraestructura |
| Summary: | Traditionally, the sensitivity analysis of a Bayesian network studies the impact of individually modifying the entries of its conditional probability tables in a one-at-a-time (OAT) fashion. However, this approach fails to give a comprehensive account of each inputs' relevance, since simultaneous perturbations in two or more parameters often entail higher-order effects that cannot be captured by an OAT analysis. We propose to conduct global variance-based sensitivity analysis instead, whereby n parameters are viewed as uncertain at once and their importance is assessed jointly. Our method works by encoding the uncertainties as n additional variables of the network. To prevent the curse of dimensionality while adding these dimensions, we use low-rank tensor decomposition to break down the new potentials into smaller factors. Last, we apply the method of Sobol to the resulting network to obtain n global sensitivity indices, one for each parameter of interest. Using a benchmark array of both expert-elicited and learned Bayesian networks, we demonstrate that the Sobol indices can significantly differ from the OAT indices, thus revealing the true influence of uncertain parameters and their interactions. |
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