Cover of: Understanding computational Bayesian statistics | William M. Bolstad

Understanding computational Bayesian statistics

  • 315 Pages
  • 3.14 MB
  • 9981 Downloads
  • English
by
Wiley , Hoboken, N.J
StatementWilliam M. Bolstad
SeriesWiley series in computational statistics, Wiley series in computational statistics
Classifications
LC ClassificationsQA279.5 .B649 2010
The Physical Object
Paginationxiv, 315 p. :
ID Numbers
Open LibraryOL24460921M
ISBN 100470046090
ISBN 139780470046098
LC Control Number2009025219
OCLC/WorldCa401167198

Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach.

Description Understanding computational Bayesian statistics PDF

With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian Cited by:   About this book A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian Understanding computational Bayesian statistics book, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach.

Publisher Description. Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian.

Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels.

It is also a valuable reference. Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach.

With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian.

Understanding Understanding computational Bayesian statistics book Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.

This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference.

8 rows    A hands-on introduction to computational statistics from a Bayesian point of view. Providing a. Computational Bayesian Statistics Book Review: Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals.

This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes.

Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work/5(4).

Understanding Computational Bayesian Statistics is anexcellent book for courses on computational statistics at theupper-level undergraduate and graduate levels. It is also avaluable reference for researchers and practitioners who usecomputer programs to conduct statistical analyses of data and solveproblems in their everyday work.

Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes.

Details Understanding computational Bayesian statistics PDF

A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this /5(4). Book description.

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and by: 2.

"Understanding computational Bayesian statistics is an excellent book for courses on computational statistics at the advanced undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.".

The style of Understanding computational Bayesian statistics is often repetitive, sentences from early paragraphs of a chapter being repeated verbatim a few pages later.

While the idea of opposing likelihood-based inference to Bayesian inference by an illustration through a dozen graphs (Chap. 1) is praiseworthy, I fear the impact is weakened by the poor 3-D readability of the graphs. Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels.

It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday : Wiley.

Bill Bolstad wrote a reply to my review of his book Understanding computational Bayesian statistics last week and here it is, unedited except for the first paragraph where he thanks me for the opportunity to respond, “so readers will see that the book has some good features beyond having a “nice cover”.” I simply processed the Word document into an html output and put a Read More bar.

He is author of Understanding Computational Bayesian Statistics, also published by Wiley. JAMES M. CURRAN is a Professor of Statistics in the Department of Statistics at.

Menu. Anniversaries; Classifieds. Music Ministries; Adminstrative; Auditions; Childcare. Understanding computational Bayesian statistics. Bolstad, William M.

John Wiley & Sons pages $ Hardcover Wiley series in computational statistics. Navigating this book. Bayes Rules. empowers readers to weave Bayesian approaches into an everyday modern practice of statistics and data science.

The overall spirit is very applied: the book utilizes modern computing resources and a reproducible pipeline; the discussion emphasizes conceptual understanding; the material is motivated by data-driven inquiry; and the delivery blends traditional.

Download Understanding computational Bayesian statistics EPUB

The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab.

Free 2-day shipping. Buy Understanding Computational Bayesian Statistics at Computational Bayesian Statistics Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals.

This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational. Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the.

Book Summary: This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology.

for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books.

The goal of this chapter is to give the reader a thorough understanding of the principles of Bayesian Parameter Estimation and its application using analytic and numerical methods. Readers interested in the broader background of Bayesian statistics may wish to consult the books by Kruschke (), Gelman et al.

(), or Jaynes (), to. This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology.Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference Understanding Bayes′ rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian.This book offers a path to get into the field of Bayesian statistics with no previous knowledge.

Building from elementary to advanced topics, including theoretic and computational aspects, and focusing on the application, it is an excellent read for newcomers to the Bayesian world.