Please use this identifier to cite or link to this item:
Title: Streamlined Variational Inference for Linear Mixed Models with Crossed Random Effects
Authors: Menictas, Marianne
Di Credico, Gioia
Wand, Matt P.
Keywords: Mean field variational BayesItem response theoryRasch analysisScalable statistical methodologySparse least squares systems
Issue Date: 2020
Publisher: EUT Edizioni Università di Trieste
Source: Marianne Menictas, Gioia Di Credico, Matt P. Wand, "Streamlined Variational Inference for Linear Mixed Models with Crossed Random Effects", Trieste, EUT Edizioni Università di Trieste, 2020
Series/Report no.: DEAMS Research Paper Series, 2020, 2
Pages: 34
We derive streamlined mean field variational Bayes algorithms for fitting linear mixed models with crossed random effects. In the most general situation, where the dimensions of the crossed groups are arbitrarily large, streamlining is hindered by lack of sparseness in the underlying least squares system. Because of this fact we also consider a hierarchy of relaxations of the mean field product restriction. The least stringent product restriction delivers a high degree of inferential accuracy. However, this accuracy must be mitigated against its higher storage and computing demands. Faster sparse storage and computing alternatives are also provided, but come with the price of diminished inferential accu-racy. This article provides full algorithmic details of three variational inference strategies, presents detailed empirical results on their pros and cons and, thus, guides the users on their choice of variational inference approach depending on the problem size and computing resources.
Type: Book
eISBN: 978-88-5511-190-4
Rights: Attribution-NonCommercial-NoDerivatives 4.0 Internazionale
Appears in Collections:DEAMS Research Paper Series 2020, 2

Files in This Item:
File Description SizeFormat
DEAMS_RP_2020_2_Menictas_DiCredico_Wand.pdfFull Text1.1 MBAdobe PDFThumbnail
DEAMS_RP_2020_2_Front_cover.jpgFront Cover213.03 kBJPEGThumbnail
Show full item record

CORE Recommender

Page view(s)

checked on Mar 29, 2023


checked on Mar 29, 2023

Google ScholarTM


This item is licensed under a Creative Commons License Creative Commons