DocumentCode :
3491049
Title :
Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting
Author :
Candela, Joaquin Quioñero ; Girard, Agathe ; Larsen, Jan ; Rasmussen, Carl Edward
Author_Institution :
Tech. Univ. Denmark, Lyngby, Denmark
Volume :
2
fYear :
2003
fDate :
6-10 April 2003
Abstract :
The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. 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.
Keywords :
Bayes methods; Gaussian distribution; Gaussian processes; forecasting theory; iterative methods; parameter estimation; prediction theory; statistical analysis; time series; Bayesian kernel models; Bayesian modelling; Gaussian distribution; Gaussian kernel shapes; Gaussian process; iterative forecasting; multiple-step ahead forecasting; predictive distribution; predictive mean; predictive variance; recursive Gaussian predictive density; relevance vector machine; reliable estimation; time-series; uncertainty; Analysis of variance; Bayesian methods; Covariance matrix; Equations; Intelligent networks; Kernel; Predictive models; Taylor series; Testing; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-7663-3
Type :
conf
DOI :
10.1109/ICASSP.2003.1202463
Filename :
1202463
Link To Document :
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