We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be ...
Inferring users' actions and intentions forms an integral part of design and development of any human-computer interface. The presence of noisy and at times ambiguous sensory ...
Models of computer users that are learned on the basis of data can make use of two types of information: data about users in general and data about the current individual user. Fo...
This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
Abstract— This paper deals with the use of Bayesian Networks to compute system reliability of complex systems under epistemic uncertainty. In the context of incompleteness of rel...