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Rapid Bayesian inference for expensive stochastic models

Abstract:

Almost all fields of science rely upon statistical inference to estimate unknown parameters in theoretical and computational models. While the performance of modern computer hardware continues to grow, the computational requirements for the simulation of models are growing even faster. This is largely due to the increase in model complexity, often including stochastic dynamics, that is necessary to describe and characterize phenomena observed using modern, high resolution, experimental techni...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Department:
MATHEMATICAL INSTITUTE
Sub department:
Mathematical Institute
Oxford college:
St Hugh's College
Role:
Author
ORCID:
0000-0002-6304-9333
Publisher:
Taylor and Francis Publisher's website
Journal:
Journal of Computational and Graphical Statistics Journal website
Volume:
31
Issue:
2
Pages:
512-528
Publication date:
2021-11-04
Acceptance date:
2021-06-29
DOI:
EISSN:
1537-2715
ISSN:
1061-8600
Language:
English
Keywords:
Pubs id:
1053803
Local pid:
pubs:1053803
Deposit date:
2021-06-29

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