Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2014-08-13 10:00
[Tutorial Lecture] Fitting linear mixed models using JAGS and Stan: A tutorial
Shravan Vasishth, Tanner Sorensen (Univ. of Potsdam) TL2014-28
Abstract (in Japanese) (See Japanese page) 
(in English) Psycholinguists routinely use linear mixed models (LMMs) for statistical inference. The most widely used tool for this purpose is the lmer function in the R library lme4. Although lmer has the advantage that models can be fit relatively quickly, one issue with this tool is that, when a full variance-covariance structure for variance components is defined, the model either fails to converge, or returns estimates of the correlation parameters that do not reflect the true underlying parameter values.
LMMs fit using a Bayesian framework have several advantages over this conventional method: A full variance-covariance matrix for random effects can be defined even in cases where lmer would fail to converge or return nonsensical estimates; the underlying generative model can be flexibly changed; and, perhaps most importantly, a direct answer to the research question can be obtained by examining the posterior distribution given data. One major barrier to using Bayesian LMMs is that it is not obvious how to use the software available for Bayesian modeling.
Although several good introductory books exist for Bayesian modeling in general, linear mixed modeling is typically treated in a fairly general way, and the more complex models that are used in psycholinguistics are usually not discussed. This tutorial provide a guide to allow researchers to quickly get started in fitting such models using the programming languages JAGS and Stan.
Keyword (in Japanese) (See Japanese page) 
(in English) Linear mixed models / Bayes Theorem / JAGS / Stan / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 176, TL2014-28, pp. 95-96, Aug. 2014.
Paper # TL2014-28 
Date of Issue 2014-08-05 (TL) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF TL2014-28

Conference Information
Committee TL  
Conference Date 2014-08-12 - 2014-08-13 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Tokyo (Komaba) 18 Bldg. Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mental Architecture for Processing and Learning of Language 
Paper Information
Registration To TL 
Conference Code 2014-08-TL 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fitting linear mixed models using JAGS and Stan: A tutorial 
Sub Title (in English)  
Keyword(1) Linear mixed models  
Keyword(2) Bayes Theorem  
Keyword(3) JAGS  
Keyword(4) Stan  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Shravan Vasishth  
1st Author's Affiliation University of Potsdam (Univ. of Potsdam)
2nd Author's Name Tanner Sorensen  
2nd Author's Affiliation University of Potsdam (Univ. of Potsdam)
3rd Author's Name  
3rd Author's Affiliation ()
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2014-08-13 10:00:00 
Presentation Time 60 minutes 
Registration for TL 
Paper # TL2014-28 
Volume (vol) vol.114 
Number (no) no.176 
Page pp.95-96 
#Pages 2 
Date of Issue 2014-08-05 (TL) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan