Thesis
Heteroscedastic Gaussian processes for uncertain and incomplete data
- Abstract:
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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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Authors
Contributors
+ Roberts, S
Role:
Supervisor
+ Jarvis, M
Role:
Supervisor
+ Aigrain, S
Role:
Examiner
+ Everson, R
Role:
Examiner
Funding
+ King Abdulaziz City for Science and Technology
More from this funder
Funding agency for:
Almosallam, I
Bibliographic Details
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
Item Description
- UUID:
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uuid:6a3b600d-5759-456a-b785-5f89cf4ede6d
- Deposit date:
- 2017-07-19
Related Items
Terms of use
- Copyright holder:
- Almosallam, I
- Copyright date:
- 2017
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