Causal models/bayesian networks
A Bayesian network is a probabilistic graphical model that can be used to describe (causal) relations between random variables in complex technical systems. Queries about the system can be answered with inference techniques that are grounded in probability theory. Expert knowledge about the system components and interaction of these components can be used to define the nodes of the graph which represent random variables. The links between the nodes represent conditional probabilities that can be learned from measurements of the system and its components. Because of their graphical structure and the clear relation of the vertices and edges with expert knowledge, Bayesian networks are more transparent and can be more easily be interpreted than neural networks in which the nodes and links do not have a direct connection with expert knowledge.
WHAT DOES TNO OFFER ON CAUSAL MODELS/BAYESIAN NETWORKS?
- TNO offers a Bayesian network approach for diagnostics and root-cause analysis of complex technical systems.
- TNO offers a systematic approach for the generation of Bayesian networks based on existing system specifications.
TNO PHM 2019 Paper: “Probabilistic Health and Mission Readiness Assessment at System-Level” (https://doi.org/10.36001/phmconf.2019.v11i1.777)