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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bayesian multilevel models for repeated measures data :Santiago Barreda and Noah Silbert.
其他題名:	a conceptual and practical introduction in R /
作者:	Barreda, Santiago,
其他作者:	Silbert, Noah H.,
面頁冊數:	1 online resource (xxii, 460 pages) :illustrations (chiefly color)
標題:	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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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).
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