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Bayesian Statistics for Beginners : A Step-By-step Approach by Therese M. Donovan and Ruth M. Mickey (2019, Trade Paperback)
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About this product
Product Identifiers
PublisherOxford University Press, Incorporated
ISBN-100198841302
ISBN-139780198841302
eBay Product ID (ePID)9038264963
Product Key Features
Number of Pages432 Pages
Publication NameBayesian Statistics for Beginners : a Step-By-Step Approach
LanguageEnglish
Publication Year2019
SubjectGeneral, Probability & Statistics / Bayesian Analysis
TypeTextbook
AuthorTherese M. Donovan, Ruth M. Mickey
Subject AreaMathematics
FormatTrade Paperback
Dimensions
Item Height0.8 in
Item Weight31.1 Oz
Item Length9.7 in
Item Width7.5 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2019-934655
ReviewsWhile 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., "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
Dewey Edition23
IllustratedYes
Dewey Decimal519.542
Table Of ContentSection 1Basics of Probability1. Introduction to Probability2. Joint, Marginal, and Conditional ProbabilitySection 2Bayes' Theorem and Bayesian Inference3. Bayes' Theorem4. Bayesian Inference5. The Author Problem - Bayesian Inference with Two Hypotheses6. The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses7. The Portrait Problem - The Portrait Problem: Bayesian Inference with Joint LikelihoodSection 3Probability Distributions8. Probability Mass Functions9. Probability Density FunctionsSection 4Bayesian Conjugates10. The White House Problem: The Beta-Binomial Conjugate11. The Shark Attack Problem: The Gamma-Poisson Conjugate12. The Maple Syrup Problem: The Normal-Normal ConjugateSection 5Monte Carlo Markov Chains (MCMC)13. The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm14. MCMC Diagnostic Approaches15. The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm16. The Maple Syrup Problem Revisited: MCMC with Gibbs SamplingSection 6Applications17. The Survivor Problem: Simple Linear Regression with MCMC18. The Survivor Problem Continued: Introduction to Bayesian Model Selection19. The Lorax Problem: Introduction to Bayesian Networks20. The Onceler Problem: Introduction to Decision TreesAppendicesAppendix 1: Beta-Binomial ConjugateAppendix 2: Gamma-Poisson ConjugateAppendix 3: Normal-Normal ConjugateAppendix 4: Simple Linear Regression ConjugatesAppendix 5: Regression Standardization in MCMC
SynopsisBayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources. Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields., 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., Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources.
LC Classification NumberQA279.5
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