Modern Bayesian network inference and its practical applicaitons in probabilistic expert systems

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One often has to make decisions under uncertainty. In computer systems, one approach to uncertainty management is to use Bayesian networks. In order to make sound decisions, Bayesian networks use probability theory as a solid mathematical foundation to answer questions (queries). They have been used by many companies, including Microsoft, NASA, Lockheed, Hewlett Packard and Nokia. To answer questions efficiently and quickly, the key is to exploit independencies holding in the problem domain. Current approaches, however, are not fully taking advantage of all available independencies. By utilizing independencies that remain unnoticed in all previous methods, we have recently suggested the first method that can precisely identify the probability information being passed in a Bayesian network. In addition, we can identify these messages significantly faster than they can be physically constructed. Our method can scout the Bayesian network in order to guide the physical computation needed to answer a question. The point to remember is that by doing so we can answer questions faster than before. It is also widely acknowledged in the Bayesian network community that beginners have great difficulty understanding query processing algorithms. The reason is that these previous algorithms are unable to identify the messages being passed in a Bayesian network when answering a question. We can. This research program will produce two major results. First, we will build a state-of-the-art computer system for using Bayesian networks in uncertainty management. Second, we will write a textbook on Bayesian networks describing our new approach. Since our approach can more precisely describe the inference process, it should make Bayesian networks accessible to a wider audience.