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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



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ID: 198172.0, MPI für biologische Kybernetik / Biologische Kybernetik
Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting
Authors:Quiñonero-Candela, J.; Girard, A.; Larsen, J.; Rasmussen, C.E.
Editors:Molina, C.; Adali, T.; Larsen, J.; Hulle, M. Van; Douglas, S.C.; Rouat, J.
Date of Publication (YYYY-MM-DD):2003
Title of Proceedings:Proceedings of 2003 IEEE International Workshop on Neural Networks for Signal Processing
Start Page:701
End Page:704
Volume (in Series):2
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables improved estimates of forecasted values and their uncertainties. In this paper we focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaussian Process and the Relevance Vector Machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting. The capability of the method is demonstrated for forecasting of time-series and compared to approximate methods.
Comment of the Author/Creator:electronical version
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:2577
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