Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Probabilistic Modelling of Uncertainty with Bayesian nonparametric Machine Learning

Tools
- Tools
+ Tools

Gadd, Charles W. L. (2018) Probabilistic Modelling of Uncertainty with Bayesian nonparametric Machine Learning. PhD thesis, University of Warwick.

[img]
Preview
PDF
WRAP_Thesis_Gadd_2018.pdf - Submitted Version - Requires a PDF viewer.

Download (19Mb) | Preview

Request Changes to record.

Abstract

This thesis addresses the use of probabilistic predictive modelling and machine learning for quantifying uncertainties. Predictive modelling makes inferences of a process from observations obtained using computational modelling, simulation, or experimentation. This is often achieved using statistical machine learning models which predict the outcome as a function of variable predictors and given process observations. Towards this end Bayesian nonparametric regression is used, which is a highly flexible and probabilistic type of statistical model and provides a natural framework in which uncertainties can be included.

The contributions of this thesis are threefold. Firstly, a novel approach to quantify parametric uncertainty in the Gaussian process latent variable model is presented, which is shown to improve predictive performance when compared with the commonly used variational expectation maximisation approach. Secondly, an emulator using manifold learning (local tangent space alignment) is developed for the purpose of dealing with problems where outputs lie in a high dimensional manifold.

Using this, a framework is proposed to solve the forward problem for uncertainty quantification and applied to two fluid dynamics simulations. Finally, an enriched clustering model for generalised mixtures of Gaussian process experts is presented, which improves clustering, scaling with the number of covariates, and prediction when compared with what is known as the alternative model. This is then applied to a study of Alzheimer’s disease, with the aim of improving prediction of disease progression.

Item Type: Thesis (PhD)
Subjects: Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Library of Congress Subject Headings (LCSH): Machine learning, Probabilities -- Mathematical models, Alzheimer's disease -- Research
Official Date: December 2018
Dates:
DateEvent
December 2018UNSPECIFIED
Institution: University of Warwick
Theses Department: School of Engineering
Thesis Type: PhD
Publication Status: Unpublished
Supervisor(s)/Advisor: Wade, Sara ; Shah, Akeel
Sponsors: Engineering and Physical Sciences Research Council ; Alzheimer's Disease Neuroimaging Initiative (Project) ; National Institutes of Health (U.S.)
Extent: xii, 167 leaves : illustrations, charts.
Language: eng
Related URLs:
  • http://webcat.warwick.ac.uk/record=b3420...

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us