Efficient state-space inference of periodic latent force models
- Submitting institution
-
Imperial College of Science, Technology and Medicine
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2236
- Type
- D - Journal article
- DOI
-
-
- Title of journal
- Journal of Machine Learning Research
- Article number
- 68
- First page
- 2337
- Volume
- 15
- Issue
- -
- ISSN
- 1532-4435
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work proposes new efficient inference algorithms that were subsequently applied to model the thermal dynamics of domestic buildings, showing that it is effective at predicting temperatures a day ahead. This provided key insights that were subsequently incorporated into the MyJoulo home heating advice system (https://www.ecs.soton.ac.uk/news/4327), which was spun out and acquired by Quby (www.quby.com) in 2015.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
