The Markov property of causal BNs provides a rational system for decomposing a large network into a set of smaller subnetworks (Figure 9). This is especially useful in the environmental and ecological sciences where the study of complex systems is usually broken down into smaller pieces, each addressed by a different group of researchers. The Markov property means that these groups can assemble separate submodels using approaches suitable for the type and scale of information they have available, and when the submodels are reassembled, the whole model will make logical, causal sense.
See also: Application of Ecological Informatics; Artificial Neural Networks: Temporal Networks; Artificial Neural Networks; Ecological Informatics: Overview; Sensitivity and Uncertainty; Statistical Prediction; Multilayer Perceptron.
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