A Bayesian network formulation for relational shape matching is presented. The main advantage of the relational shape matching approach is the obviation of the non-rigid spatial m...
Anand Rangarajan, James M. Coughlan, Alan L. Yuill...
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 ...
Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: ge...
Jiang Su, Harry Zhang, Charles X. Ling, Stan Matwi...
We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial ...
This paper describes how high level biological knowledge obtained from ontologies such as the Gene Ontology (GO) can be integrated with low level information extracted from a Baye...