Pseudo-marginal Bayesian inference for Gaussian Processes
- Submitting institution
-
University of Glasgow
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-01095
- Type
- D - Journal article
- DOI
-
10.1109/TPAMI.2014.2316530
- Title of journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Article number
- -
- First page
- 2214
- Volume
- 36
- Issue
- 11
- ISSN
- 0162-8828
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2014
- URL
-
http://eprints.gla.ac.uk/93194/
- 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
-
1
- Research group(s)
-
-
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- ORIGINALITY: the paper addresses major methodological challenges in using Gaussian Process priors (an important class of flexible models) in Bayesian inference and shows that the proposed approach outperforms state of the art techniques developed for the same purpose. SIGNIFICANCE: appears in a premier Machine Intelligence journal. RIGOUR: the experiments are performed over five standard benchmarks, allowing direct comparison with the state of the art. Code is publicly available (http://www.eurecom.fr/~filippon/Code/code_pseudo_marg.tar.gz)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -