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 be a mixture of irregularly sampled higher derivatives of the signal of interest.
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Murray-Smith, R., Pearlmutter, B.A. (2005). Transformations of Gaussian Process Priors. In: Winkler, J., Niranjan, M., Lawrence, N. (eds) Deterministic and Statistical Methods in Machine Learning. DSMML 2004. Lecture Notes in Computer Science(), vol 3635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559887_7
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DOI: https://doi.org/10.1007/11559887_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29073-5
Online ISBN: 978-3-540-31728-9
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