Latent Bayesian melding for integrating individual and population models
- Submitting institution
-
University of Aberdeen
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 171264721
- Type
- E - Conference contribution
- DOI
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10.5555/2969442.2969643
- Title of conference / published proceedings
- 29th Annual Conference on Neural Information Processing Systems, NIPS 2015
- First page
- 3618
- Volume
- 2
- Issue
- -
- ISSN
- 1049-5258
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2015
- 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
-
2
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposed a new and generic theoretical method called Latent Bayesian Melding (LBM) for integrating prior belief models and the posterior models. LBM is an extension to Bayesian Melding (BM) in Statistics. Introducing Bayesian melding approach into machine learning is fairly new, and we proved that the popular method of posterior regularization (PR) in the machine learning community indeed is a special case of BM, although PR was proposed ten years after BM. Although LBM was applied to energy disaggregation, as a generic method it can be applied to any other domains, e.g., medical imaging data
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
