# Introduction to Probability with R

Kenneth Baclawski

January 24, 2008 by Chapman and Hall/CRC
Textbook - 380 Pages - 92 B/W Illustrations
ISBN 9781420065213 - CAT# C6521
Series: Chapman & Hall/CRC Texts in Statistical Science

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### Features

• Uses R programs and animations to convey important aspects of probability and to encourage experimentation
• Covers the theorems of probability along with stochastic processes and the relationships among them
• Deals with probabilistic reasoning in chapters on statistics and conditional probability
• Introduces transforms via randomization, a unique approach to a very important subject
• Explores entropy and information to demonstrate basic stochastic processes and the most commonly occurring distributions
• Shows how Markov chains are a versatile tool for modeling natural phenomena
• Includes many exercises and selected answers
• Offers R programs, PowerPoint slides for presentations and lectures, and related web links on a supplementary website

### Summary

Based on a popular course taught by the late Gian-Carlo Rota of MIT, with many new topics covered as well, Introduction to Probability with R presents R programs and animations to provide an intuitive yet rigorous understanding of how to model natural phenomena from a probabilistic point of view. Although the R programs are small in length, they are just as sophisticated and powerful as longer programs in other languages. This brevity makes it easy for students to become proficient in R.

This calculus-based introduction organizes the material around key themes. One of the most important themes centers on viewing probability as a way to look at the world, helping students think and reason probabilistically. The text also shows how to combine and link stochastic processes to form more complex processes that are better models of natural phenomena. In addition, it presents a unified treatment of transforms, such as Laplace, Fourier, and z; the foundations of fundamental stochastic processes using entropy and information; and an introduction to Markov chains from various viewpoints. Each chapter includes a short biographical note about a contributor to probability theory, exercises, and selected answers.