Bayes' Rule with Python: A Tutorial Introduction to Bayesian Analysis

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9780993367939: Bayes' Rule with Python: A Tutorial Introduction to Bayesian Analysis

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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"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.

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Descripción Jim Stone, United States, 2016. Paperback. Estado de conservación: New. Language: English . Brand New Book ***** Print on Demand *****.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. de la librería AAV9780993367939

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Descripción Jim Stone, United States, 2016. Paperback. Estado de conservación: New. Language: English . Brand New Book ***** Print on Demand *****. 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. de la librería AAV9780993367939

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Descripción Sebtel Press. Paperback. Estado de conservación: New. Paperback. 188 pages. Dimensions: 9.0in. x 6.0in. x 0.4in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Nº de ref. de la librería 9780993367939

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Descripción Jim Stone, 2016. Paperback. Estado de conservación: New. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. 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. de la librería LIE9780993367939

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