Current Bayesian net representations do not consider structure in the domain and include all variables in a homogeneous network. At any time, a human reasoner in a large domain ma...
This paper describes a novel approach to network management topologies where multiple customized topologies are self-configured, self-optimized, and maintained automatically by the...
Liam Fallon, Daryl Parker, Martin Zach, Markus Lei...
— We present K2GA, an algorithm for learning Bayesian network structures from data. K2GA uses a genetic algorithm to perform stochastic search, while employing a modified versio...
The task of eliciting all probabilities required for a Bayesian network can be supported by first acquiring qualitative constraints on the numerical quantities to be obtained. Buil...
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvant...