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Thesis

Towards efficient Bayesian inference: Cox processes and probabilistic integration

Abstract:

In this thesis we present a variety of new, continuous, Bayesian Gaussian-process-driven Cox process models. These are used to model sparse event data distributed on a continuous domain, where the events may have a tendency to cluster. These find direct use in application areas ranging from disease incidence modelling through to statistical cosmology, where the distribution of galaxies in the universe is weakly clustered due to the effects of dark matter. They may also be deployed in ...

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Division:
MPLS
Department:
Engineering Science
Department:
University of Oxford
Role:
Author

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Department:
University of Oxford
Role:
Supervisor
Role:
Supervisor
Natural Environment Research Council More from this funder
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
UUID:
uuid:32ccb7f8-6eaf-420f-bf97-113d3504dfa5
Deposit date:
2018-07-08

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