State space modeling of time series
State space modeling of time series
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
A Comparison of New and Old Algorithms for a Mixture EstimationProblem
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Robust probabilistic inference in distributed systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Update rules for parameter estimation in Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation as a basis for communication in sensor networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Uncertainty processing is a core task in many applications of distributed systems. Typical distributed systems have local processing nodes to collect information, which usually contain uncertainties, and do computational work. The nodes can interact with each other, and they evolve with time. A promising way of modelling and processing uncertainties in these systems is to use graphical models to form beliefs about the required information. Dynamic probabilistic networks for distributed uncertainty processing are presented in this paper. Two approaches are given, and comparison shows that the model with the more state-of-art approach performs better. Since it is not possible to obtain enough knowledge to construct an exact model at the beginning, the model needs to adjust itself when evolving. Therefore we have developed a parameter update algorithm to make the model adapt to the changing environment. Experiments are presented to show the effectiveness of the models and the algorithms.