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"Belief networks are used to model uncertainty in a domain. The term "belief networks" encompasses a whole range of different but related techniques which deal with reasoning under uncertainty. Both quantitative (mainly using Bayesian probabilistic methods) and qualitative techniques are used. Increasingly, belief network techniques are being employed to deliver advanced knowledge based systems to solve real world problems. Belief networks are particularly useful for diagnostic applications and have been used in many deployed systems. The free-text help facility in the Microsoft Office product employs Bayesian belief network technology." (Ian Harrison)
Bayesian Belief Networks (BBNs) are at the cutting edge of expert systems research and development. Unlike the traditional rule-based approach to expert systems, they are able to replicate the essential features of plausible reasoning (reasoning under conditions of uncertainty) in a consistent, efficient and mathematically sound way. Critically they are able to retract belief in a particular case when the basis of that belief is explained away by new evidence. Since most real life problems involve inherently uncertain relationships, BBN is a technology with huge potential for application across many domains.
This page provides some comments and links on probabilistic network theory and systems. It is primarily for information on belief networks (BNs) which are also known as graphical models, Bayesian networks, Bayesian Belief Networks (BBNs), Markov networks, Chain graphs and Causal Probabilistic Networks (some of these are names for variants).
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