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Introduction to Probability with R (Texts in Statistical Science (Chapman & Hall/CRC)) (Chapman & Hall/CRC Texts in Statistical Science)
 
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Introduction to Probability with R (Texts in Statistical Science (Chapman & Hall/CRC)) (Chapman & Hall/CRC Texts in Statistical Science) (Hardcover)

by Kenneth P. Baclawski (Author)
3.0 out of 5 stars  See all reviews (1 customer review)
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  • This item: Introduction to Probability with R (Texts in Statistical Science (Chapman & Hall/CRC)) (Chapman & Hall/CRC Texts in Statistical Science) by Kenneth P. Baclawski

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    This item Delivered FREE in the UK with Super Saver Delivery. See details and conditions

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Product details

  • Hardcover: 384 pages
  • Publisher: Chapman & Hall; 1 edition (12 Feb 2008)
  • Language English
  • ISBN-10: 1420065211
  • ISBN-13: 978-1420065213
  • Product Dimensions: 23.6 x 15.7 x 1.8 cm
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon.co.uk Sales Rank: 767,131 in Books (See Bestsellers in Books)

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Product Description

Product Description

This text presents R programs and animations to provide an intuitive yet rigorous understanding of how to model natural phenomena from a probabilistic point of view. It centers on viewing probability as a way to look at the world and shows how to combine and link stochastic processes to form more complex processes that are better models of natural phenomena. The text also covers the Poisson process, transforms, Bayesian networks, entropy and information, and Markov chains. Each chapter includes a short biographical note about a contributor to probability theory, exercises, and selected answers. Ancillary material is accessible online.


From the Back Cover

Based on the popular probability course by Gian-Carlo Rota of MIT, Probability and Random Processes with R provides a calculus-based introduction to probability. The text systemically motivates and organizes the standard distributions that most often occur in probability using physical processes. Presenting a probabilistic approach that builds on other approaches such as geometry and physical processes, the book addresses sets, events, and probability; finite processes; random variables; statistics and normal distribution; conditional probability; the Poisson process; entropy and information; Markov chains; Markov processes; Bayesian networks; and the Bayesian web. Various exercises and examples compare different perspectives.

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1 of 1 people found the following review helpful:
3.0 out of 5 stars Not enough R, 14 Dec 2008
By Andrew Dalby "ardalby" (oxford) - See all my reviews
(REAL NAME)   
While many books cover using R for statistical analysis and inference there are not many textbooks that focus on the use of R for probability. So from the title I was hoping that this book would fill that gap.

The book is well written and I enjoyed the biographical introductions to each of the chapters. The book is based around the courses the author has delivered in probability over the last 30 years and it is a mature course on probability. There are many exercises and solutions to selected exercises which make it more useful to students.

Ultimately for me the problem I have with the book is that there is not enough R coding in it. The first few chapters have some nice R examples although you probably need to know something of R to get the best of them. Later chapters have very little R in them. For example the chapter on Markov Chains only has one piece of R code. Considering that MCMC is one of the most significant applications of R this is disappointing.
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