| Introduction to Probability with R Contributor(s): Baclawski, Kenneth (Author) |
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ISBN: 1420065211 ISBN-13: 9781420065213 Publisher: CRC Press OUR PRICE: $133.00 Product Type: Hardcover - Other Formats Published: January 2008 Annotation: Based on the popular probability course by Gian-Carlo Rota of MIT, Introduction to Probability 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. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Bayesian Analysis - Technology & Engineering | Operations Research |
| Dewey: 519.202 |
| LCCN: 2007041288 |
| Series: Texts in Statistical Science (Chapman & Hall/CRC) |
| Physical Information: 1" H x 6.47" W x 9.28" (1.48 lbs) 380 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. The book has an accompanying website with more information. |