Guided incremental construction of belief networks Export

@misc{citeulike:758037, abstract = {Because uncertain reasoning is often intractable, it is hard to reason with a large amount of knowledge. One solution to this problem is to specify a set of possible models, some simple and some complex, and choose which to use based on the problem. We present an architecture for interpreting temporal data, called AIID, that incrementally constructs belief networks based on data that arrives asynchronously. It synthesizes the opportunistic control of the blackboard architecture with...}, author = {Sutton, C. and Burns, B. and Morrison, C. and Cohen, P.}, citeulike-article-id = {758037}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5313}, keywords = {bayesian, bayesnet, learning, network, structure}, posted-at = {2006-07-13 19:45:05}, priority = {2}, title = {Guided incremental construction of belief networks}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5313}, year = {2003} }

TY - GEN ID - citeulike:758037 L3 - citeulike-article-id:758037 N2 - Because uncertain reasoning is often intractable, it is hard to reason with a large amount of knowledge. One solution to this problem is to specify a set of possible models, some simple and some complex, and choose which to use based on the problem. We present an architecture for interpreting temporal data, called AIID, that incrementally constructs belief networks based on data that arrives asynchronously. It synthesizes the opportunistic control of the blackboard architecture with... TI - Guided incremental construction of belief networks KW - bayesian KW - bayesnet KW - learning KW - network KW - structure AU - Sutton, C AU - Burns, B AU - Morrison, C AU - Cohen, P PY - 2003/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5313 ER -