In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. These restrictions may codify expert knowledge in a g...
An increasing number of tasks require people to explore, navigate and search extremely complex data sets visualized as graphs. Examples include electrical and telecommunication ne...
Nelson Wong, M. Sheelagh T. Carpendale, Saul Green...
Wireless sensor networks are typically ad-hoc networks of resource-constrained nodes; in particular, the nodes are limited in power resources. It can be difficult and costly to rep...
Fatemeh Kazemeyni, Einar Broch Johnsen, Olaf Owe, ...
Several recent techniques for solving Markov decision processes use dynamic Bayesian networks to compactly represent tasks. The dynamic Bayesian network representation may not be g...
As some cognitive research suggests, in the process of learning languages, in addition to overt explicit negative evidence, a child often receives covert explicit evidence in form...