A Bayesian network is a probabilistic model that relates random variables and their conditional dependency statements. The model uses a directed acyclical graph (DAG) that has nodes and edges that describe the conditional probabilities on the nodes. There are many medical examples of Bayesian networks such as diseases that have common symptoms and diagnostic tests. Diagnosis can make use of these conditional dependent statements. In this thesis, the basic graph theory and probability theory used when working with Bayesian networks is explained in detail. Along with this, the process described by S. L. Lauritzen and D.J. Spiegelhalter to calculate random variable probabilities and rapid absorption of evidence into the entire network is clearly documented. This process was also coded into software and is provided. This process was coded and is available in a CD-ROM. The CD-ROM, an appendix to the thesis, is available for viewing at the Media Center of Library and Information Access.
