Approximate learning in complex dynamic Bayesian networks
UNSPECIFIED (1999) Approximate learning in complex dynamic Bayesian networks. In: 15th Conference on Uncertainty in Artificial Intelligence, ROYAL INST TECHNOL, STOCKHOLM, SWEDEN, JUL 30-AUG 01, 1999. Published in: UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS pp. 585-593.Full text not available from this repository.
In this paper we extend the work of Smith and Papamichail (1999) and present fast approximate Bayesian algorithms for learning in complex scenarios where at any time frame, the relationships between explanatory state space variables can be described by a Bayesian network that evolve dynamically over time and the observations taken are not necessarily Gaussian. It uses recent developments in approximate Bayesian forecasting methods in combination with more familiar Gaussian propagation algorithms on junction trees. The procedure for learning state parameters from data is given explicitly for common sampling distributions and the methodology is illustrated through a real application. The efficiency of the dynamic approximation is explored by using the Hellinger divergence measure and theoretical bounds for the efficacy of such a procedure are discussed.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Journal or Publication Title:||UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS|
|Publisher:||MORGAN KAUFMANN PUB INC|
|Editor:||Laskey, KB and Prade, H|
|Number of Pages:||9|
|Page Range:||pp. 585-593|
|Title of Event:||15th Conference on Uncertainty in Artificial Intelligence|
|Location of Event:||ROYAL INST TECHNOL, STOCKHOLM, SWEDEN|
|Date(s) of Event:||JUL 30-AUG 01, 1999|
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