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Thesis

Heteroscedastic Gaussian processes for uncertain and incomplete data

Abstract:

In probabilistic inference, many implicit and explicit assumptions are taken about the nature of input noise and the function fit to either simplify the mathematics, improve the time complexity or optimise for space. It is often assumed that the inputs are noiseless or that the noise is drawn from the same distribution for all inputs, that all the variables used during training will be present during prediction and with the same degrees of uncertainties, and that the confidence about the p...

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Supervisor
Role:
Supervisor
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Examiner
Role:
Examiner
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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