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Bayesian Statistics for Beginners

a step-by-step approach

Therese M. Donovan and Ruth M. Mickey

May 2019

ISBN: 9780198841302

432 pages
Paperback
246x189mm

In Stock

Price: £41.99

This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.

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Description

This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.

  • Provides a quick read for the novice student of Bayesian statistics
  • Assumes some prior knowledge of basic algebra, but all mathematical content and equations are accompanied by explanatory prose
  • Adopts an informal question and answer approach which make for a fun and light-hearted approach to the topic whilst testing and consolidating student learning

About the Author(s)

Therese M. Donovan, Wildlife Biologist, U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit, University of Vermont, USA, and Ruth M. Mickey, Professor Emerita, Department of Mathematics and Statistics, University of Vermont, USA

Therese Donovan is a wildlife biologist with the U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit. Based in the Rubenstein School of Environment and Natural Resources at the University of Vermont, Therese teaches graduate courses on ecological modeling and conservation biology. She works with a variety of student and professional collaborators on research problems focused on the conservation of vertebrates. Therese is the Director of the Vermont Cooperative Fish and Wildlife Unit Spreadsheet Project, a suite of on-line tutorials in Excel and R for modeling and analysis of wildlife populations. She lives in Vermont with her husband, Peter, and two children, Evan and Ana.

Ruth Mickey is a Professor Emerita of Statistics at the University of Vermont. Most of Ruth's career was spent in the Department of Mathematics and Statistics, where she taught courses in Applied Multivariate Analysis, Categorical Data, Survey Sampling, Analysis of Variance and Regression, and Probability. She served as an advisor or committee member of numerous MS and PhD committees over a broad range of academic disciplines. She worked on the development of statistical methods and applications to advance public health and natural resources issues throughout her career.

Table of Contents

    Section 1
    Basics of Probability
    1:Introduction to Probability
    2:Joint, Marginal, and Conditional Probability
    Section 2
    Bayes' Theorem and Bayesian Inference
    3:Bayes' Theorem
    4:Bayesian Inference
    5:The Author Problem - Bayesian Inference with Two Hypotheses
    6:The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses
    7:The Portrait Problem: Bayesian Inference with Joint Likelihood
    Section 3
    Probability Functions
    8:Probability Mass Functions
    9:Probability Density Functions
    Section 4
    Bayesian Conjugates
    10:The White House Problem: The Beta-Binomial Conjugate
    11:The Shark Attack Problem: The Gamma-Poisson Conjugate
    12:The Maple Syrup Problem: The Normal-Normal Conjugate
    Section 5
    Markov Chain Monte Carlo
    13:The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm
    14:MCMC Diagnostic Approaches
    15:The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm
    16:The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling
    Section 6
    Applications
    17:The Survivor Problem: Simple Linear Regression with MCMC
    18:The Survivor Problem Continued: Introduction to Bayesian Model Selection
    19:The Lorax Problem: Introduction to Bayesian Networks
    20:The Once-ler Problem: Introduction to Decision Trees
    Appendices
    Appendix 1: The Beta-Binomial Conjugate Solution
    Appendix 2: The Gamma-Poisson Conjugate Solution
    Appendix 3: The Normal-Normal Conjugate Solution
    Appendix 4: Conjugate Solutions for Simple Linear Regression
    Appendix 5: The Standardization of Regression Data

Reviews

"While reading this book, I joined the authors on a learning endeavor thanks to their honesty and intellectual vulnerability. Their lack of experience with Bayesian statistics helps them to be effective communicators . . . If you are interested in starting your Bayesian journey, then Bayesian Statistics for Beginners is an excellent place to begin." - Taylor Saucier, Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, The Journal of Wildlife Management

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