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Thesis

Variance reduction techniques for chemical reaction network simulation

Abstract:

In recent decades stochastic models have become an indispensable tool when analysing quantitative biological data, which are often subject to noise, both from intrinsic and extrinsic sources. Though such models generate richer, and perhaps more realistic, behaviour than their counterpart deterministic models, they are also inherently more difficult to study. Since exact solutions describing the dynamics of stochastic models are scarce, one is often forced to resort to simulation of these ...

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Sub department:
Mathematical Institute
Research group:
WCMB
Oxford college:
New College
Role:
Author
ORCID:
0000-0002-9164-6565

Contributors

Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Sub department:
Mathematical Institute
Research group:
WCMB
Oxford college:
St Hugh's College
Role:
Supervisor
ORCID:
0000-0002-6304-9333
Role:
Examiner
Institution:
Uppsala University
Research group:
Integrative Scalable Computing Laboratory
Role:
Examiner
ORCID:
0000-0001-7273-7923
More from this funder
Programme:
Clarendon Fund Awards
Funding agency for:
Beentjes, C
More from this funder
Programme:
Clarendon scholarship
Funding agency for:
Beentjes, C
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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