dc.contributor.author |
Xing, H |
en |
dc.contributor.author |
Nicholls, G |
en |
dc.contributor.author |
Lee, Jeong |
en |
dc.date.accessioned |
2020-08-24T04:41:11Z |
en |
dc.date.available |
2020-06-22 |
en |
dc.date.available |
2020-08-24T04:41:11Z |
en |
dc.date.issued |
2020-07-04 |
en |
dc.identifier.citation |
Proceedings of Machine Learning Research: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). Association for Uncertainty in Artificial Intelligence. 124. 04 Jul 2020 |
|
dc.identifier.uri |
http://hdl.handle.net/2292/52771 |
en |
dc.description.abstract |
Current literature on posterior approximation for Bayesian inference offers many alternative methods. Does our chosen approximation scheme work well on the observed data? The best existing generic diagnostic tools treating this kind of question by looking at performance averaged over data space, or otherwise lack diagnostic detail. However, if the approximation is bad for most data, but good at the observed data, then we may discard a useful approximation. We give graphical diagnostics for posterior approximation at the observed data. We estimate a “distortion map” that acts on univariate marginals of the approximate posterior to move them closer to the exact posterior, without recourse to the exact posterior. |
en |
dc.publisher |
Association for Uncertainty in Artificial Intelligence |
en |
dc.relation.ispartof |
The 36th Conference on Uncertainty in Artificial Intelligence |
en |
dc.relation.ispartofseries |
The 36th Conference on Uncertainty in Artificial Intelligence |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
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dc.title |
Distortion estimates for approximate Bayesian inference |
en |
dc.type |
Conference Item |
en |
dc.rights.holder |
Copyright: The author |
en |
pubs.author-url |
https://proceedings.mlr.press/v124/xing20b.html |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RetrictedAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
805168 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
Statistics |
en |
pubs.record-created-at-source-date |
2020-07-04 |
en |