Introduction to Probability

The text grew out of lectures given at Harvard in statistics courses and aims to give readers the vocabulary and methods needed to think about statistics, chance, and uncertainty. Its examples span a wide range, from everyday coincidences and logical puzzles to Google's PageRank algorithm and Markov chain Monte Carlo (MCMC). Further applications reach into genetics, medicine, computer science, and information theory. Joseph K. Blitzstein, with co-author Jessica Hwang, writes in an approachable manner and grounds each idea in a concrete real-world situation.

A recurring method is to explain distributions through short stories, which reveal how the standard probability models relate to one another. The authors also rely on conditioning, breaking a hard problem into smaller parts that are easier to handle. Readers will find many intuitive explanations, diagrams, and practice problems throughout.

The final section of each chapter shows how to carry out the relevant simulations and calculations in R, a free, open-source statistical environment. The printed edition comes with a code for free access to an eBook version.