Learning With Graphical Models Export

@techreport{citeulike:758002, abstract = {Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper explains how these models can be extended to machine learning,...}, address = {NASA Ames Research Center}, author = {Buntine, W. L.}, citeulike-article-id = {758002}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.8946}, keywords = {bayesian, bayesnet, graphicalmodels, learning, network}, number = {FIA-94-02}, posted-at = {2006-07-13 18:42:52}, priority = {2}, title = {Learning With Graphical Models}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.8946}, year = {1994} }

TY - RPRT ID - citeulike:758002 L3 - citeulike-article-id:758002 N2 - Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper explains how these models can be extended to machine learning,... IS - FIA-94-02 AD - NASA Ames Research Center TI - Learning With Graphical Models KW - bayesian KW - bayesnet KW - graphicalmodels KW - learning KW - network AU - Buntine, WL PY - 1994/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.8946 ER -