Bayesian networks are used to show and calculate the effects of pieces of knowledge on each other. They are strongly related to expert systems, but use probability theory to calculate those effects and can therefore easily deal with problems like uncertainty and missing data.- Category ID : 54099
Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
Article published in JAIR (Journal of AI Research) about a way to implement belief networks by compiling networks into arithmetic expressions and then answering queries using an evaluation algorithm.
Slides and additional notes from a tutorial by Nir Friedman and Daphne Koller on automated learning of belief networks, given at the Neural Information Processing Systems (NIPS-2001) conference