In this paper, we study the problem of information preservation when decomposing a single Bayesian network into a set of smaller Bayesian networks. We present a method that lossle...
Many perception, reasoning, and learning problems can be expressed as Bayesian inference. We point out that formulating a problem as Bayesian inference implies specifying a probabi...
This article contributes a generic model of topic models. To define the problem space, general characteristics for this class of models are derived, which give rise to a represent...
As real-world Bayesian networks continue to grow larger and more complex, it is important to investigate the possibilities for improving the performance of existing algorithms of ...
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...