BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Bayes' Rule With R: A Tutorial Introduction to Bayesian Analysis

Bayes' Rule With R: A Tutorial Introduction to Bayesian Analysis

Bayes' Rule With R: A Tutorial Introduction to Bayesian Analysis
Stock photo: cover may vary

Bayes' Rule With R: A Tutorial Introduction to Bayesian Analysis Softcover - 2016

by Stone, James V

Add to wish list
  • Used
  • very good
  • Paperback
Used - Very good

Description

Sebtel Press, 2016. Softcover. Very Good.
Ask the seller a question Add to wish list
A$90.18
Free Delivery within USA
Standard delivery: 3 to 14 days
More delivery options
Ships from The Book Mansion (Maine, United States)

Details

  • Title Bayes' Rule With R: A Tutorial Introduction to Bayesian Analysis
  • Author Stone, James V
  • Binding Paperback
  • Condition Used - Very good
  • Pages 186
  • Volumes 1
  • Language ENG
  • Publisher Sebtel Press
  • Publication date 2016
  • Features Glossary
  • Bookseller's Inventory # 9780993367946-VG
  • ISBN 9780993367946 / 0993367941
  • Weight 0.56 lbs (0.25 kg)
  • Dimensions 9 x 6 x 0.4 in (22.86 x 15.24 x 1.02 cm)
  • Category Mathematics
  • Quantity available 1

About The Book Mansion Maine, United States

Biblio member since 2020

We have been selling books for 14 years, we have several warehouses across the US. Shipments are prepared and dispatched every day including weekends.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from The Book Mansion

Reader reviews for Bayes' Rule With R: A Tutorial Introduction to Bayesian Analysis

From the publisher

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 R (3.2) code snippets, which reproduce key numerical results and diagrams.

tracking-