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Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting

2003

Conference Paper

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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.

Author(s): Quiñonero-Candela, J. and Girard, A. and Larsen, J. and Rasmussen, CE.
Journal: IEEE International Conference on Acoustics, Speech and Signal Processing
Volume: 2
Pages: 701-704
Year: 2003
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: IEEE International Conference on Acoustics, Speech and Signal Processing

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2686,
  title = {Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting},
  author = {Quiñonero-Candela, J. and Girard, A. and Larsen, J. and Rasmussen, CE.},
  journal = {IEEE International Conference on Acoustics, Speech and Signal Processing},
  volume = {2},
  pages = {701-704},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2003}
}