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 real-life 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.
helps readers develop problem-solving 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, power-law 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
Chapter-by-chapter summaries and hundreds of practical exercises
is an ideal text for a beginning course in probability at the undergraduate level.