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Bayesian Statistics for Beginners
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Bayesian Statistics for Beginners : a step-by-step approach

by Ruth M. Mickey , Therese M. Donovan

Book Details

Format Paperback / Softback
ISBN-10 0198841302
ISBN-13 9780198841302
Publisher Oxford University Press
Imprint Oxford University Press
Country of Manufacture GB
Country of Publication GB
Publication Date May 29th, 2019
Print length 432 Pages
Weight 922 grams
Dimensions 19.00 x 24.60 x 2.10 cms
Product Classification: Bayesian inference Maths for scientists
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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. 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.

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