An introduction to probability at the undergraduate level
Chance and randomness are encountered on a daily basis. Authored by a highly qualified professor in the field, Probability: With Applications and R delves into the theories and applications essential to obtaining a thorough understanding of probability.
With reallife examples and thoughtful exercises from fields as diverse as biology, computer science, cryptology, ecology, public health, and sports, the book is accessible for a variety of readers. The book’s emphasis on simulation through the use of the popular R software language clarifies and illustrates key computational and theoretical results.
Probability: With Applications and R helps readers develop problemsolving skills and delivers an appropriate mix of theory and application. The book includes:

Chapters covering first principles, conditional probability, independent trials, random variables, discrete distributions, continuous probability, continuous distributions, conditional distribution, and limits

An early introduction to random variables and Monte Carlo simulation and an emphasis on conditional probability, conditioning, and developing probabilistic intuition

An R tutorial with example script files

Many classic and historical problems of probability as well as nontraditional material, such as Benford’s law, powerlaw distributions, and Bayesian statistics

A topics section with suitable material for projects and explorations, such as random walk on graphs, Markov chains, and Markov chain Monte Carlo

Chapterbychapter summaries and hundreds of practical exercises
Probability: With Applications and R is an ideal text for a beginning course in probability at the undergraduate level.