The nature of statistical learning theory
The nature of statistical learning theory
Stochastic partial differential equations: a modeling, white noise functional approach
Stochastic partial differential equations: a modeling, white noise functional approach
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Digital Image Processing
Distributed Parameter Systems: Identification, Estimation and Control
Distributed Parameter Systems: Identification, Estimation and Control
Inverse Problem Theory and Methods for Model Parameter Estimation
Inverse Problem Theory and Methods for Model Parameter Estimation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Process Gauss-Newton for non-parametric simultaneous localization and mapping
International Journal of Robotics Research
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In this paper we shall discuss an extension to Gaussian process (GP) regression models, where the measurements are modeled as linear functionals of the underlying GP and the estimation objective is a general linear operator of the process. We shall show how this framework can be used for modeling physical processes involved in measurement of the GP and for encoding physical prior information into regression models in form of stochastic partial differential equations (SPDE). We shall also illustrate the practical applicability of the theory in a simulated application.