Journal article
Probabilistic inference on noisy time series (PINTS)
- Abstract:
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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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(Version of record, pdf, 1.5MB)
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- Publisher copy:
- 10.5334/jors.252
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Authors
Funding
Bibliographic Details
- 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:
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2049-9647
- Source identifiers:
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1028713
Item Description
- Keywords:
- Pubs id:
-
pubs:1028713
- UUID:
-
uuid:24f58bc7-5da1-4b1a-88ce-1e12c9c1e550
- Local pid:
- pubs:1028713
- Deposit date:
- 2019-07-08
Terms of use
- Copyright holder:
- Clerx et al
- Copyright date:
- 2019
- Notes:
- © The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Licence:
- CC Attribution (CC BY)
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