In many practical parameter estimation problems, the appropriate criterion is periodic in the parameter space. This paper considers the mean square periodic error (MSPE) criterion...
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal pat...
Gregory F. Cooper, Denver Dash, John Levander, Wen...
Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applicati...
Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it. Mathematical models fo...
Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass