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Doing Bayesian
Data Analysis
A Tutorial with R, JAGS,
and Stan
EDITION
2
JOHN K. KRUSCHKE
Dept. of Psychological and Brain Sciences
Indiana University, Bloomington
AMSTERDAM • BOSTON • HEIDELBERG • LONDON
NEW YORK • OXFORD • PARIS • SAN DIEGO
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Copyright © 2015, 2011 Elsevier Inc. All rights reserved.
No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical,
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found at our website: www.elsevier.com/permissions.
This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as
may be noted herein).
Notices
Knowledge and best practice in this eld are constantly changing. As new research and experience broaden our
understanding, changes in research methods, professional practices, or medical treatment may become necessary.
Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any
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be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.
To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for
any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any
use or operation of any methods, products, instructions, or ideas contained in the material herein.
ISBN: 978-0-12-405888-0
Library of Congress Cataloging-in-Publication Data
Kruschke, John K.
Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan / John K. Kruschke. – 2E [edition].
pages cm
Includes bibliographical references.
ISBN 978-0-12-405888-0
1. Bayesian statistical decision theory. 2. R (Computer program language) I. Title.
QA279.5.K79 2014
519.5
42–dc23
2014011293
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
For information on all Academic Press publications
visit our website at store.elsevier.com
Dedicated to my mother, Marilyn A. Kruschke,
and to the memory of my father, Earl R. Kruschke,
both of whom brilliantly exemplied and taught sound reasoning.
And, in honor of my father,
who dedicated his rst book to his children,
I also dedicate this book to mine:
Claire A. Kruschke and Loren D. Kruschke.
CONTENTS
1. What’s in This Book (Read This First!) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 RealPeopleCanReadThisBook.................................................. 1
1.2 What’sinThisBook.............................................................. 3
1.3 What’sNewintheSecondEdition?............................................... 6
1.4 GimmeFeedback(BePolite)...................................................... 8
1.5 ThankYou!..................................................................... 8
PARTI TheBasics:Models,Probability,Bayes’Rule,andR........... 13
2. Introduction:Credibility,Models,andParameters............................. 15
2.1 Bayesian Inference Is Reallocation of Credibility Across Possibilities . ................... 16
2.2 PossibilitiesAreParameterValuesinDescriptiveModels............................. 22
2.3 TheStepsofBayesianDataAnalysis............................................... 25
2.4 Exercises....................................................................... 31
3. TheRProgrammingLanguage............................................... 33
3.1 GettheSoftware................................................................ 35
3.2 ASimpleExampleofRinAction.................................................. 36
3.3 BasicCommandsandOperatorsinR.............................................. 38
3.4 VariableTypes.................................................................. 42
3.5 LoadingandSavingData........................................................ 53
3.6 SomeUtilityFunctions........................................................... 56
3.7 ProgramminginR............................................................... 61
3.8 GraphicalPlots:OpeningandSaving.............................................. 69
3.9 Conclusion..................................................................... 69
3.10 Exercises....................................................................... 70
4. WhatIsThisStuCalledProbability?......................................... 71
4.1 TheSetofAllPossibleEvents..................................................... 72
4.2 Probability:OutsideorInsidetheHead............................................ 73
4.3 ProbabilityDistributions......................................................... 78
4.4 Two-WayDistributions........................................................... 89
4.5 Appendix:RCodeforFigure4.1................................................... 93
4.6 Exercises....................................................................... 95
vii
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