Midlands State University Library
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A student's guide to Bayesian statistics created by Ben Lambert

By: Material type: TextTextLanguage: English Publication details: London Sage 2018Description: 498 pages ill. 25 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781473916364
  • 1473916364
  • 9781473916357
  • 1473916356
Subject(s): LOC classification:
  • QA279.5
Contents:
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.
Summary: "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." --
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Core Collection Main Library Core Collection QA279.5 LAM (Browse shelf(Opens below)) 150010 Available BK135905

Includes bibliographical references and index

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.

"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." --

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