Thesis
Towards efficient Bayesian inference: Cox processes and probabilistic integration
- Abstract:
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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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Authors
Contributors
+ Roberts, S
Department:
University of Oxford
Role:
Supervisor
+ Osborne, M
Role:
Supervisor
Funding
Natural Environment Research Council
More from this funder
Bibliographic Details
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
Item Description
- UUID:
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uuid:32ccb7f8-6eaf-420f-bf97-113d3504dfa5
- Deposit date:
- 2018-07-08
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Terms of use
- Copyright holder:
- Gunter, T
- Copyright date:
- 2017
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