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Journal article

Probabilistic inference on noisy time series (PINTS)

Abstract:

Time series models are ubiquitous in science, arising in any situation where researchers seek to understand how a system’s behaviour changes over time. A key problem in time series modelling is inference; determining properties of the underlying system based on observed time series. For both statistical and mechanistic models, inference involves finding parameter values, or distributions of parameters values, which produce outputs consistent with observations. A wide variety of inference te...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.5334/jors.252

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0003-4062-3061
More by this author
Institution:
University of Oxford
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Computer Science
Role:
Author
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Publisher:
Ubiquity Press Publisher's website
Journal:
Journal of Open Research Software Journal website
Volume:
7
Issue:
1
Article number:
23
Publication date:
2019-07-19
Acceptance date:
2019-07-05
DOI:
EISSN:
2049-9647
Source identifiers:
1028713
Keywords:
Pubs id:
pubs:1028713
UUID:
uuid:24f58bc7-5da1-4b1a-88ce-1e12c9c1e550
Local pid:
pubs:1028713
Deposit date:
2019-07-08

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