Assessment and adjustment of approximate inference algorithms using the law of total variance

10.1080/10618600.2021.1880921

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Bibliographic Details
Main Authors:	Xuejun Yu, David Nott, Minh-Ngoc Tran, Nadja Klein
Other Authors:	STATISTICS AND DATA SCIENCE
Format:	Article
Published:	Taylor & Francis 2022
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/214927
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-214927
record_format	dspace
spelling	sg-nus-scholar.10635-2149272024-04-03T05:47:30Z Assessment and adjustment of approximate inference algorithms using the law of total variance Xuejun Yu David Nott Minh-Ngoc Tran Nadja Klein STATISTICS AND DATA SCIENCE STATISTICS & APPLIED PROBABILITY 10.1080/10618600.2021.1880921 Journal of Computational and Graphical Statistics 30 4 977-990 2022-02-07T07:23:38Z 2022-02-07T07:23:38Z 2021-03-18 Article Xuejun Yu, David Nott, Minh-Ngoc Tran, Nadja Klein (2021-03-18). Assessment and adjustment of approximate inference algorithms using the law of total variance. Journal of Computational and Graphical Statistics 30 (4) : 977-990. ScholarBank@NUS Repository. https://doi.org/10.1080/10618600.2021.1880921 1061-8600 https://scholarbank.nus.edu.sg/handle/10635/214927 Taylor & Francis Taylor & Francis
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
description	10.1080/10618600.2021.1880921
author2	STATISTICS AND DATA SCIENCE
author_facet	STATISTICS AND DATA SCIENCE Xuejun Yu David Nott Minh-Ngoc Tran Nadja Klein
format	Article
author	Xuejun Yu David Nott Minh-Ngoc Tran Nadja Klein
spellingShingle	Xuejun Yu David Nott Minh-Ngoc Tran Nadja Klein Assessment and adjustment of approximate inference algorithms using the law of total variance
author_sort	Xuejun Yu
title	Assessment and adjustment of approximate inference algorithms using the law of total variance
title_short	Assessment and adjustment of approximate inference algorithms using the law of total variance
title_full	Assessment and adjustment of approximate inference algorithms using the law of total variance
title_fullStr	Assessment and adjustment of approximate inference algorithms using the law of total variance
title_full_unstemmed	Assessment and adjustment of approximate inference algorithms using the law of total variance
title_sort	assessment and adjustment of approximate inference algorithms using the law of total variance
publisher	Taylor & Francis
publishDate	2022
url	https://scholarbank.nus.edu.sg/handle/10635/214927
_version_	1795374865166893056

Assessment and adjustment of approximate inference algorithms using the law of total variance

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