Books like Probability and Computing by Michael Mitzenmacher

A practical guide that presents key algorithms as reusable design patterns, offering ready‑to‑use code snippets in multiple languages for software developers and engineers.

A concise primer on probability theory and stochastic models for science, engineering, economics and related disciplines.

A foundational guide for undergraduate students that introduces elementary probability theory and stochastic processes, and demonstrates their application across engineering, computer science, management science, and the physical and social sciences.

Now available in a fully revised and updated second edition, this well established textbook provides a straightforward introduction to the theory of probability. The presentation is entertaining without any sacrifice of rigour; important notions are covered with the clarity that the subject demands. Topics covered include conditional probability, independence, discrete and continuous random variables, basic combinatorics, generating functions and limit theorems, and an introduction to Markov chains. The text is accessible to undergraduate students and provides numerous worked examples and exercises to help build the important skills necessary for problem solving.

The leading reference on probabilistic methods in combinatorics-now expanded and updated When it was first published in 1991, The Probabilistic Method became instantly the standard reference on one of the most powerful and widely used tools in combinatorics. Still without competition nearly a decade later, this new edition brings you up to speed on recent developments, while adding useful exercises and over 30% new material. It continues to emphasize the basic elements of the methodology, discussing in a remarkably clear and informal style both algorithmic and classical methods as well as modern applications. The Probabilistic Method, Second Edition begins with basic techniques that use expectation and variance, as well as the more recent martingales and correlation inequalities, then explores areas where probabilistic techniques proved successful, including discrepancy and random graphs as well as cutting-edge topics in theoretical computer science. A series of proofs, or "probabilistic lenses," are interspersed throughout the book, offering added insight into the application of the probabilistic approach. New and revised coverage includes: Several improved as well as new results A continuous approach to discrete probabilistic problems Talagrand's Inequality and other novel concentration results A discussion of the connection between discrepancy and VC-dimension Several combinatorial applications of the entropy function and its properties A new section on the life and work of Paul Erd's-the developer of the probabilistic method