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Bayesian multilevel models for repeated measures data :a conceptual and practical introduction in R /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian multilevel models for repeated measures data :Santiago Barreda and Noah Silbert.
Reminder of title:	a conceptual and practical introduction in R /
Author:	Barreda, Santiago,
other author:	Silbert, Noah H.,
Description:	1 online resource (xxii, 460 pages) :illustrations (chiefly color)
Subject:	Statistics -- Multivariate analysis -- Directional data; spatial statistics.
ISBN:	9781003285878$q(ebook)

Bayesian multilevel models for repeated measures data :a conceptual and practical introduction in R /
Barreda, Santiago,

Bayesian multilevel models for repeated measures data :a conceptual and practical introduction in R /Santiago Barreda and Noah Silbert. - 1 online resource (xxii, 460 pages) :illustrations (chiefly color)

Includes bibliographical references and index.

Introduction: Experiments and variables -- Probabilities, likelihood, and inference -- Fitting Bayesian regression models with brms -- Inspecting a 'single group' of observations using a Bayesian multilevel model -- Comparing two groups of observations : factors and contrasts -- Variation in parameters ('random effects') and model comparison -- Comparing many groups, interactions, and posterior predictive checks -- Varying variances, more about priors, and prior predictive checks -- Quantitative predictors and their interactions with factors -- Logistic regression and signal detection theory models -- Multiple quantitative predictors, dealing with large models, and Bayesian ANOVA -- Multinominal and ordinal regression -- Writing up experiments : an investigation of the perception of apparent speaker characteristics from speech acoustics.

"This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book"--

ISBN: 9781003285878$q(ebook)

LCCN: 2022053169Subjects--Topical Terms:

966336
Statistics -- Multivariate analysis -- Directional data; spatial statistics.

LC Class. No.: QA279.5 / .B37 2023

Dewey Class. No.: 519.5/42

Bayesian multilevel models for repeated measures data :a conceptual and practical introduction in R /
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245 1 0 $a Bayesian multilevel models for repeated measures data : $b a conceptual and practical introduction in R / $c Santiago Barreda and Noah Silbert.
264 1 $a Abingdon, Oxon ; $a New York, NY : $b Routledge, $c 2023.
264 4 $c ©2023
300 $a 1 online resource (xxii, 460 pages) : $b illustrations (chiefly color)
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504 $a Includes bibliographical references and index.
505 0 $a Introduction: Experiments and variables -- Probabilities, likelihood, and inference -- Fitting Bayesian regression models with brms -- Inspecting a 'single group' of observations using a Bayesian multilevel model -- Comparing two groups of observations : factors and contrasts -- Variation in parameters ('random effects') and model comparison -- Comparing many groups, interactions, and posterior predictive checks -- Varying variances, more about priors, and prior predictive checks -- Quantitative predictors and their interactions with factors -- Logistic regression and signal detection theory models -- Multiple quantitative predictors, dealing with large models, and Bayesian ANOVA -- Multinominal and ordinal regression -- Writing up experiments : an investigation of the perception of apparent speaker characteristics from speech acoustics.
520 $a "This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book"-- $c Provided by publisher.
588 $a Description based on online resource; title from digital title page (viewed on August 02, 2023).
650 7 $a Statistics -- Multivariate analysis -- Directional data; spatial statistics. $2 msc $3 966336
650 7 $a Computer science -- Theory of data -- Data structures. $2 msc $3 966337
650 7 $a Mathematics education -- Computer science applications -- Data bases, information systems. $2 msc $3 966338
650 7 $a Computer science -- Theory of data. $2 msc $3 966339
650 0 $a Bayesian statistical decision theory. $3 182005
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650 0 $a Multilevel models (Statistics) $3 240063
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650 6 $a R (Langage de programmation) $3 943796
650 6 $a Modé les multiniveaux (Statistique) $3 966392
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650 7 $a Statistics -- Parametric inference -- Bayesian inference. $2 msc $3 966333
650 7 $a Statistics -- Applications -- Applications to psychology. $2 msc $3 966334
650 7 $a Probability theory and stochastic processes -- Special processes -- Interacting random processes; statistical mechanics type models; percolation theory. $2 msc $3 966335
700 1 $a Silbert, Noah H., $e author. $3 966330
776 0 8 $i Print version: $a Barreda, Santiago. $t Bayesian multilevel models for repeated measures data. $d Abingdon, Oxon ; New York, NY : Routledge, 2023 $z 9781032259628 $w (DLC) 2022053168 $w (OCoLC)1349451898
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