Hierarchical Bayesian Networks: an Approach

@misc{citeulike:758005, abstract = {Bayesian Networks are one of the most popular formalisms for reasoning under uncertainty. Hierarchical Bayesian Networks (HBNs) are an extension of Bayesian Networks that are able to deal with structured domains, using knowledge about the structure of the data to introduce a bias that can contribute to improving inference and learning methods. In effect, nodes in an HBN are (possibly nested) aggregations of simpler nodes. Every aggregate node is itself an HBN modeling independences inside ...}, author = {And, To C. }, citeulike-article-id = {758005}, keywords = {bayesian, bayesnet, hierarchical, network}, posted-at = {2006-07-13 18:45:12}, priority = {2}, title = {Hierarchical Bayesian Networks: an Approach}, url = {http://citeseer.ist.psu.edu/743804.html} }

TY - GEN ID - citeulike:758005 L3 - citeulike-article-id:758005 N2 - Bayesian Networks are one of the most popular formalisms for reasoning under uncertainty. Hierarchical Bayesian Networks (HBNs) are an extension of Bayesian Networks that are able to deal with structured domains, using knowledge about the structure of the data to introduce a bias that can contribute to improving inference and learning methods. In effect, nodes in an HBN are (possibly nested) aggregations of simpler nodes. Every aggregate node is itself an HBN modeling independences inside ... TI - Hierarchical Bayesian Networks: an Approach KW - bayesian KW - bayesnet KW - hierarchical KW - network AU - And, To UR - http://citeseer.ist.psu.edu/743804.html ER -