Introduction to Probability with R

Introduction to Probability with R is a book that uses R programs and animations to provide an intuitive yet rigorous understanding of how to model natural phenomena from a probabilistic point of view. It is organized around key themes, such as viewing probability as a way to look at the world, combining and linking stochastic processes, and presenting a unified treatment of transforms, entropy, and information. Each chapter includes a short biographical note about a contributor to probability theory, exercises, and selected answers.

Format: Paperback / softback
Length: 380 pages
Publication date: 21 January 2023
Publisher: Taylor & Francis Ltd

Introduction to Probability with R is a comprehensive and engaging textbook that offers a thorough introduction to probability theory using the powerful programming language R. Authored by the late Gian-Carlo Rota, a renowned professor at MIT, this textbook combines theoretical concepts with practical applications, making it an ideal resource for both students and professionals in the field.

One of the key strengths of Introduction to Probability with R is its emphasis on providing an intuitive yet rigorous understanding of probability concepts. The textbook employs a wide range of R programs and animations to illustrate these concepts, making them accessible and engaging for students with varying backgrounds. These programs are concise yet powerful, allowing students to quickly become proficient in R and apply its techniques to solve real-world problems.

The textbook is organized into several chapters, each focusing on a specific aspect of probability theory. The first chapter provides an overview of probability concepts, including random variables, probability distributions, and conditional probability. It also introduces the basic principles of probability, such as Bayes' theorem and the law of large numbers.

The second chapter delves into the concept of stochastic processes, which are used to model natural phenomena. The text explains how to combine and link stochastic processes to form more complex processes that are better models of natural phenomena. It also presents a unified treatment of transforms, such as Laplace, Fourier, and z, as well as the foundations of fundamental stochastic processes using entropy and information.

The third chapter introduces Markov chains, a fundamental tool in probability theory. The text presents Markov chains from various viewpoints, including discrete-time and continuous-time models, and discusses their applications in fields such as finance, biology, and social sciences. Each chapter includes a short biographical note about a contributor to probability theory, providing insight into the historical development of the field.

In addition to its theoretical content, Introduction to Probability with R includes a wealth of exercises and selected answers to help students reinforce their understanding of the material. The accompanying website offers additional resources, such as lecture videos, code examples, and solutions to the exercises.

Overall, Introduction to Probability with R is a highly recommended textbook for anyone interested in learning probability theory. Its combination of theoretical insights, practical applications, and engaging presentation style makes it an invaluable resource for students, researchers, and professionals in fields such as mathematics, statistics, engineering, and computer science.

Weight: 700g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032477800