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Bayes' Rule With Python: A Tutorial Introduction to Bayesian Analysis: Volume 4 (A Tutorial Introduction Book) - Tapa blanda
Sinopsis
Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of Bayesian analysis.Note that this book includes Python (3.0) code snippets, which reproduce key numerical results and diagrams.
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Críticas
"An accessible introduction to Bayesian analysis for those with little mathematical experience." Journal of the Royal Statistical Society, 2015. "An excellent book ... highly recommended. " CHOICE: Academic Reviews Online, February 2014. "Short, interesting, and very easy to read, Bayes' Rule serves as an excellent primer for students and professionals ... " Top Ten Math Books On Bayesian Analysis, July 2014. "An excellent first step for readers with little background in the topic. " Computing Reviews, June 2014. "A crackingly clear tutorial for beginners. Exactly the sort of book required for those taking their first steps in Bayesian analysis." Dr Paul A. Warren. School of Psychological Sciences, University of Manchester. "This book is short and eminently readable. It introduces the Bayesian approach to addressing statistical issues without using any advanced mathematics, which should make it accessible to students from a wide range of backgrounds, including biological and social sciences." Dr Devinder Sivia. Lecturer in Mathematics, St John's College, Oxford University, and author of Data Analysis: A Bayesian Tutorial. "For those with a limited mathematical background, Stone's book provides an ideal introduction to the main concepts of Bayesian analysis. " Dr Peter M Lee. Department of Mathematics, University of York. Author of Bayesian Statistics: An Introduction. "Bayesian analysis involves concepts which can be hard for the uninitiated to grasp. Stone's patient pedagogy and gentle examples convey these concepts with uncommon lucidity. " Dr Charles Fox. Department of Computer Science, University of Sheffield.
Reseña del editor
Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of Bayesian analysis. Note that this book includes Python (3.0) code snippets, which reproduce key numerical results and diagrams.
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Bayes' Rule with Python : A Tutorial Introduction to Bayesian Analysis
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Bayes' Rule With Python: A Tutorial Introduction to Bayesian Analysis
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Sebtel Press, 2016
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ISBN 13: 9780993367939
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Bayes' Rule With Python: A Tutorial Introduction to Bayesian Analysis (A Tutorial Introduction Book)
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ISBN 13: 9780993367939
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of Bayesian analysis. Note that this book includes Python (3.0) code snippets, which reproduce key numerical results and diagrams. Nº de ref. del artículo: 9780993367939
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