Inference in belief networks: A procedural guide Export

@article{citeulike:758004, abstract = {Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on inference algorithms to compute beliefs in the context of observed evidence. One established method for exact inference on belief networks is the Probability Propagation in Trees of Clusters (PPTC) algorithm, as developed by Lauritzen and Spiegelhalter and refined by Jensen et al. PPTC converts the belief network into a secondary structure, then computes probabilities by manipulating the...}, author = {Huang, C. and Darwiche, A.}, citeulike-article-id = {758004}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.3279}, journal = {International Journal of Approximate Reasoning}, keywords = {bayesian, bayesnet, inference, network}, number = {3}, pages = {225--263}, posted-at = {2006-07-13 18:43:32}, priority = {2}, title = {Inference in belief networks: A procedural guide}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.3279}, volume = {15}, year = {1996} }

TY - JOUR ID - citeulike:758004 L3 - citeulike-article-id:758004 N2 - Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on inference algorithms to compute beliefs in the context of observed evidence. One established method for exact inference on belief networks is the Probability Propagation in Trees of Clusters (PPTC) algorithm, as developed by Lauritzen and Spiegelhalter and refined by Jensen et al. PPTC converts the belief network into a secondary structure, then computes probabilities by manipulating the... IS - 3 JF - International Journal of Approximate Reasoning EP - 263 TI - Inference in belief networks: A procedural guide VL - 15 SP - 225 KW - bayesian KW - bayesnet KW - inference KW - network AU - Huang, C AU - Darwiche, A PY - 1996/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.3279 ER -