PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Transformations of Gaussian Process priors
Barak A. Pearlmutter and Roderick Murray-Smith
In: Deterministic and Statistical methods in machine learning (2005) Springer , Germany , pp. 110-123.

Abstract

Abstract. Gaussian process prior systems generally consist of noisy measurements of samples of the putatively Gaussian process of interest, where the samples serve to constrain the posterior estimate. Here we consider the case where the measurements are instead noisy weighted sums of samples. This framework incorporates measurements of derivative information and of filtered versions of the process, thereby allowing GPs to perform sensor fusion and tomography; allows certain group invariances (ie symmetries) to be weakly enforced; and under certain conditions suitable application allows the dataset to be dramatically reduced in size. The method is applied to a sparsely sampled image, where each sample is taken using a broad and non-monotonic point spread function. It is also applied to nonlinear dynamic system identification applications where a nonlinear function is followed by a known linear dynamic system, and where observed data can

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1268
Deposited By:Roderick Murray-Smith
Deposited On:28 November 2005