Bayesian networks are probabilistic graphical models widely employed in AI for the implementation of knowledge-based systems. Standard inference algorithms can update the beliefs a...
The objective of this paper is to describe the use of a probabilistic approach to Web effort estimation by means of a Bayesian Network. A Bayesian Network is a model that embodies ...
This paper deals with the problem of inference under uncertain information. This is a generalization of a paper of Cardona et al. (1991a) where rules were not allowed to contain n...
In the paper we combine a Bayesian Network model for encoding forensic evidence during a given time interval with a Hidden Markov Model (EBN-HMM) for tracking and predicting the de...
Olivier Y. de Vel, Nianjun Liu, Terry Caelli, Tib&...
Networks are becoming a unifying framework for modeling complex systems and network inference problems are frequently encountered in many fields. Here, I develop and apply a gener...