Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/25839Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Roy, G | - |
| dc.contributor.author | Chakrabarty, D | - |
| dc.coverage.spatial | Indore, India | - |
| dc.date.accessioned | 2023-01-22T10:27:27Z | - |
| dc.date.available | 2023-01-22T10:27:27Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier | ORCID iD: Dalia Chakrabarty https://orcid.org/0000-0003-1246-4235 | - |
| dc.identifier.citation | Roy, G. and Chakrabarty, D. (2023) 'Efficient Uncertainty Quantification for Under-constraint Prediction following Learning using MCMC', Communications in Computer and Information Science (CCIS), 0 (accepted, in press), pp. 1 - 13. | en_US |
| dc.identifier.issn | 1865-0929 | - |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25839 | - |
| dc.format.extent | 1 - 13 | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | Springer Nature | en_US |
| dc.rights | Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/[insert DOI] | - |
| dc.rights.uri | https://www.springernature.com/gp/open-research/policies/journal-policies | - |
| dc.source | 29th International Conference on Neural Information Processing (ICONIP 2022) | - |
| dc.source | 29th International Conference on Neural Information Processing (ICONIP 2022) | - |
| dc.subject | covariance kernel hyperparameter | en_US |
| dc.subject | MCMC | en_US |
| dc.subject | learning under constraint | en_US |
| dc.subject | Gaussian process | en_US |
| dc.subject | uncertainty propagation | en_US |
| dc.title | Efficient Uncertainty Quantification for Under-constraint Prediction following Learning using MCMC | en_US |
| dc.type | Conference Paper | en_US |
| dc.relation.isPartOf | Communications in Computer and Information Science (CCIS) | - |
| pubs.finish-date | 2022-11-26 | - |
| pubs.finish-date | 2022-11-26 | - |
| pubs.publication-status | Accepted | - |
| pubs.start-date | 2022-11-22 | - |
| pubs.start-date | 2022-11-22 | - |
| dc.rights.holder | The Author(s) | - |
| Appears in Collections: | Dept of Mathematics Embargoed Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Embragoed until publication | 1.07 MB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.