Probability

<p>An introduction to probability at the undergraduate level</p> <p>Chance and randomness are encountered on a daily basis. Authored by a highly qualified professor in the field, <i>Probability: With Applications and R</i> delves into the theories and applications essential to obtaining a thorough understanding of probability.</p> <p>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.</p> <p><i>Probability: With Applications and R</i> helps readers develop problem-solving skills and delivers an appropriate mix of theory and application. The book includes:</p> <ul> <li>Chapters covering first principles, conditional probability, independent trials, random variables, discrete distributions, continuous probability, continuous distributions, conditional distribution, and limits</li> <li>An early introduction to random variables and Monte Carlo simulation and an emphasis on conditional probability, conditioning, and developing probabilistic intuition</li> <li>An R tutorial with example script files</li> <li>Many classic and historical problems of probability as well as nontraditional material, such as Benford’s law, power-law distributions, and Bayesian statistics</li> <li>A topics section with suitable material for projects and explorations, such as random walk on graphs, Markov chains, and Markov chain Monte Carlo</li> <li>Chapter-by-chapter summaries and hundreds of practical exercises</li> </ul> <p><i>Probability: With Applications and R</i> is an ideal text for a beginning course in probability at the undergraduate level.</p>