Once the structure and conditional probabilities of a BN have been specified (using prior knowledge, models, data-based learning, or a combination), the network can be used to determine the probability distributions of specific target or query nodes, given findings (either deterministic or probabilistic observations) for other nodes. When the query nodes are descendants of the nodes with findings, this process is called prediction. When they are ancestors, it is called inference (or diagnosis). For example, using the network in Figure 3, one can predict hypoxia (H) given values (or distributions) for nutrient concentration (N) and season (S), or one can infer the value of N from findings on H (and/or any other variables).
The network may also be used to determine the most probable explanation for why particular values for some system variables were observed, to accurately describe the effects ofinterventions (or external controls) on the system, and to support decisions about management actions in the face of uncertainty. Each of these will be described in the following subsections.
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