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Thesis

Bayesian nonparametric methods and applications in statistical network modelling

Abstract:

Bayesian Statistics provide us with a powerful approach to model real-world phenomena and quantify the uncertainty therein by treating unknown factors as random. The Bayesian paradigm prescribes quantifying the prior knowledge about some state of the world, and after having obtained new information updating that knowledge in order to update the prior beliefs and propose posterior knowledge. A central approach to Bayesian Statistics is modelling, i.e. to represent a data generating process ...

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Division:
MPLS
Department:
Statistics
Sub department:
Statistics
Oxford college:
St Peter's College
Role:
Author

Contributors

Role:
Supervisor
ORCID:
0000-0002-3952-224X
Role:
Supervisor
Role:
Examiner
Role:
Examiner
More from this funder
Name:
Engineering and Physical Sciences Research Council
Funder identifier:
http://dx.doi.org/10.13039/501100000266
Funding agency for:
Miscouridou, X
Grant:
EP/N509711/1
EP/L016710/1
EP/M508111/1
Programme:
EPSRC Doctoral Training Partnerships
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Name:
A.G. Leventis Foundation
Funder identifier:
http://dx.doi.org/10.13039/501100004117
Funding agency for:
Miscouridou, X
Programme:
Educational Grants Scheme
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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