Bayesian Networks [expert systems] by W. Premchaiswaid

By W. Premchaiswaid

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Moreover, in the case of Katrina it was not clear who had the authority to order evacuations as necessary. Weather prediction centers, such as the hurricane center in Miami generally do a very good job in plotting the path of the impending hurricane and its severity. We can attach a probability of severity due to an impending hurricane using the weather predictions over a planning horizon. An application of a Dynamic Bayesian Network for predicting the Quality-of-Life (QOL) of displaced citizens due to a hurricane, such as Katrina is shown in Figure 5.

The concept of conditional probability is very useful because there are numerous “real-world” examples where the probability of one event is conditional on the probability of a previous event. 5. Characterization of an extreme event In the financial world, extreme events are termed “extraordinary items” which are defined as unusual in nature AND infrequent in its occurrence (Kieso, Weygandt and Warfield, 2007). Using this information, let us define an extreme event as an incident, that is; (a) unusual in nature AND/OR (b) infrequent in its occurrence.

Selecting an evidence corresponding to the node Fig. 9. Specifying the number of the expert(s) We propose a methodology based on group decision making for weighting expert opinions or the degree of an expert’s belief in identifying the causal relationships between variables in a BN model. The idea is to find the final BN solution that is obtained from a group of Building a Bayesian Network Model Based on the Combination of Structure Learning Algorithms and Weighting Expert Opinions Scheme 29 experts and to minimize the number of relationships among the nodes in the model for simplicity by setting a threshold value.

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