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Bayesian Statistics

An Introduction
Book Hero Magic crafted this summary to help describe this book. While it's new and still learning, it may not be perfect - your feedback is welcome! Summary
Bayesian Statistics by Peter M. Lee introduces readers to the Bayesian approach to statistics, where probabilities represent degrees of belief or understanding. The book provides an accessible entry into Bayesian principles, showcasing how these methods differ from traditional statistical techniques and emphasising practical applications in various fields. It includes thorough explanations of key concepts, supported by real-world examples to illustrate the effectiveness of Bayesian inferencing.
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Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

If youโ€™re fascinated by the intersection of probability and statistics and enjoy exploring mathematical frameworks for decision making under uncertainty, you might find this book captivating. It delves into the principles and applications of Bayesian methods, making it ideal for those seeking to deepen their understanding of statistical analysis and its real-world implications in science and nature.

Book Hero thinking about your next read

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques.

Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! Description

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Leeโ€™s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques.

This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation, and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notions such as sufficiency are brought out of a discussion of the salient features of specific examples.

This edition:

  • Includes expanded coverage of Gibbs sampling, including more numerical examples and treatments of OpenBUGS, R2WinBUGS, and R2OpenBUGS.
  • Presents significant new material on recent techniques such as Bayesian importance sampling, variational Bayes, Approximate Bayesian Computation (ABC), and Reversible Jump Markov Chain Monte Carlo (RJMCMC).
  • Provides extensive examples throughout the book to complement the theory presented.
  • Accompanied by a supporting website featuring new material and solutions.

More and more students are realising that they need to learn Bayesian statistics to meet their academic and professional goals. This book is best suited for use as a main text in courses on Bayesian statistics for third and fourth year undergraduates and postgraduate students.

Book Details

INFORMATION

ISBN: 9781118332573

Publisher: John Wiley & Sons Inc

Format: Paperback / softback

Date Published: 03 August 2012

Country: United States

Imprint: John Wiley & Sons Inc

Edition: 4th edition

Audience: Professional and scholarly

DIMENSIONS

Spine width: 23.0mm

Width: 154.0mm

Height: 230.0mm

Weight: 621g

Pages: 496

About the Author

Peter Lee, Department of Mathematics & Formerly Provost of Wentworth College, University of York.

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