A First Course in Bayesian Statistical Methods - Springer...

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Springer Texts in StatisticsSeries Editors:G. CasellaS. FienbergI. OlkinFor other titles published in this series, go to
Peter D. HoffA First Course in BayesianStatistical Methods123
Peter D. HoffDepartment of StatisticsUniversity of WashingtonSeattle WA 98195-4322USA[email protected]ISSN 1431-875XISBN 978-0-387-92299-7e-ISBN 978-0-387-92407-6DOI 10.1007/978-0-387-92407-6Springer Dordrecht Heidelberg London New YorkLibrary of Congress Control Number: 2009929120c Springer Science+Business Media, LLC 2009All rights reserved. This work may not be translated or copied in whole or in part without the writtenpermission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York,NY10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use inconnection with any form of information storage and retrieval, electronic adaptation, computer software,or by similar or dissimilar methodology now known or hereafter developed is forbidden.The use in this publication of trade names, trademarks, service marks, and similar terms, even if they arenot identified as such, is not to be taken as an expression of opinion as to whether or not they are subject toproprietary rights.Printed on acid-free paperSpringer is part of Springer Science+Business Media ()
PrefaceThis book originated from a set of lecture notes for a one-quarter graduate-level course taught at the University of Washington. The purpose of the courseis to familiarize the students with the basic concepts of Bayesian theory andto quickly get them performing their own data analyses using Bayesian com-putational tools. The audience for this course includes non-statistics graduatestudents who did well in their department’s graduate-level introductory statis-tics courses and who also have an interest in statistics. Additionally, first- andsecond-year statistics graduate students have found this course to be a usefulintroduction to statistical modeling. Like the course, this book is intended tobe a self-contained and compact introduction to the main concepts of Bayesiantheory and practice. By the end of the text, readers should have the ability tounderstand and implement the basic tools of Bayesian statistical methods fortheir own data analysis purposes. The text is not intended as a comprehen-sive handbook for advanced statistical researchers, although it is hoped thatthis latter category of readers could use this book as a quick introduction toBayesian methods and as a preparation for more comprehensive and detailedstudies.ComputingMonte Carlo summaries of posterior distributions play an important role inthe way data analyses are presented in this text. My experience has beenthat once a student understands the basic idea of posterior sampling, theirdata analyses quickly become more creative and meaningful, using relevantposterior predictive distributions and interesting functions of parameters. Theopen-sourceR

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Term
Spring
Professor
Vannucci,Marina
Tags
Statistics, Probability theory, Bayesian probability, Bayesian statistics, prior distribution

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