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  Prediction at an Uncertain Input for Gaussian Processes and Relevance Vector Machines - Application to Multiple-Step Ahead Time-Series Forecasting

Quiñonero-Candela, J., Girard, A., & Rasmussen, C.(2003). Prediction at an Uncertain Input for Gaussian Processes and Relevance Vector Machines - Application to Multiple-Step Ahead Time-Series Forecasting (IMM-2003-18). Kopenhagen, Denmark: Technical University of Denmark, DTU: Informatics and Mathematical Modelling.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DDF6-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-753B-6
Genre: Report

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Quiñonero-Candela, J, Author              
Girard, A, Author
Rasmussen, CE1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: This report non-linear models that map an input D-dimensional column vector x into a single dimensional output f(x). The non-linear mapping f() is implemented by means of a Gaussian process (GP) or a Relevance Vector Machine (RVM), see for example [Rasmussen, 1996] and [Tipping, 2001]. We are given a training data set D = fx i ; y i g N i=1 where the target y i relates to the input x i through y i = f(x i ) + (1) where N (0; ) is additive i.i.d. Gaussian noise of variance.

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 Dates: 2003-10
 Publication Status: Published in print
 Pages: 14
 Publishing info: Kopenhagen, Denmark : Technical University of Denmark, DTU: Informatics and Mathematical Modelling
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: IMM-2003-18
BibTex Citekey: 2578
 Degree: -

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