By W. Premchaiswaid

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**Additional resources for Bayesian Networks [expert systems]**

**Example text**

0. 2 WMATA example In order to illustrate this example, an artificial real-life scenario is constructed as follows: It is a clear and sunny Monday morning and you decide to take the WMATA metro subway system to work. m. meeting and your boss is also attending. m. C. via the “Yellow Line” (see Figure 1). In 10 minutes, you will be in the office. You will have enough time to get your coffee and to discuss the Washington Redskins victory over the Dallas Cowboys in yesterday’s game. Suddenly, the train abruptly stops!

One such area is the Central Virginia Seismic Zone, where the August 23, 2011 earthquake occurred. Unlike the state of California or the continent of Japan, Virginia is not located near the edge of a tectonic plate. Although the bedrock in this zone has no major faults, it is loaded with smaller faults that occurred when the Appalachian Mountains were formed. Although it has been 114 years since a major earthquake of this magnitude has occurred, the August 23rd quake was a stark reminder that we can no longer assume the Alfred E.

Xi 1 , )kX p( xi i , ) kX for the observations xi ki , xi X . , xi 1 , )kX p( xi i , ) kX given Y kY . For a person with current state information, p( X Y , ) then denotes the "Generalized Probability Density Function" (gpdf) for X, given all possible observations of Y. The joint gpdf over U is the gpdf for U. A Bayesian network for domain U represents a joint gpdf over U. This representation consists of a set of local conditional gpdfs combined with a set of conditional independence assertions that allow the construction of a global gpdf from the local gpdfs.