A Student's Guide to BAYESIAN STATISTICS

Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws u...

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Bibliographic Details
Main Author: Lambert, Ben (Author)
Format: Book
Language:English
Published: Los Angeles SAGE 2018
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Online Access:Click Here to View Status and Holdings.
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100 1 # |a Lambert, Ben  |e author 
245 1 0 |a A Student's Guide to BAYESIAN STATISTICS  |c Ben Lambert 
264 # 1 |a Los Angeles  |b SAGE  |c 2018 
264 # 4 |c ©2018 
300 # # |a xx, 498 pages  |b illustrations  |c 25 cm. 
336 # # |a text  |2 rdacontent 
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338 # # |a volume  |2 rdacarrier 
504 # # |a Includes bibliographical references (pages 489-491) and index 
505 0 # |a An introduction to Bayesian inference -- Understanding the Bayesian formula -- Analytic Bayesian methods -- A practical guide to doing real-life Bayesian analysis: Computational Bayes -- Hierarchical models and regression 
520 # # |a Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference, Understanding Bayes' rule, Nuts and bolts of Bayesian analytic methods, Computational Bayes and real-world Bayesian analysis, Regression analysis and hierarchical methods. This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses. 
650 # 0 |a Bayesian statistical decision theory 
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