Probability and Statistics for Computer Scientists, Third Edition | Probability and Statistics for Computer Scientists

Probability and Statistics for Computer Scientists, Third Edition

Some books teach probability as an abstract ritual. This one keeps the computer scientist in view. Michael Baron’s Third Edition is designed for readers who want to model uncertainty, simulate random systems, and reason statistically about computation. It moves from foundational probability to random variables and distributions, then into Monte Carlo simulation and stochastic processes.

From Basic Probability to Working Models

Early chapters establish events, sample spaces, set operations, axioms, conditional probability, and independence. The book then builds the language of random variables: discrete and continuous distributions, expectation, variance, covariance, correlation, and familiar families such as Bernoulli, binomial, geometric, negative binomial, Poisson, uniform, exponential, gamma, and normal. The central limit theorem gets its own place in the discussion.

Simulation, Monte Carlo, and Stochastic Processes

A major strength is the computational thread. The text covers random number generation, inverse transform and rejection methods, generation of random vectors, and using Monte Carlo methods to estimate probabilities, means, standard deviations, lengths, areas, volumes, and integrals. Later chapters introduce stochastic processes, Markov chains, matrix approaches, steady-state distributions, and counting processes including binomial and Poisson processes, with simulation techniques continuing to support the theory.

What You’ll Find Inside

• Clear development of probability axioms and set operations
• Discrete and continuous random variables, expectations, and variance
• Binomial, Poisson, normal, exponential, gamma, and related distributions
• Monte Carlo methods and random variable simulation
• Markov chains, steady-state analysis, and counting processes
• Applications that connect theory to computing and modeling

Who This Book Is For

Computer science students, data science learners, and professionals who need a rigorous but applied introduction to probability and statistics will find the book’s computing-oriented examples and simulation focus especially relevant. It assumes mathematical maturity but keeps the motivation tied to computational problems.

Why It Belongs on Your Digital Shelf

If your work involves algorithms, data, simulation, or systems where randomness matters, probability is not optional. This Third Edition gives you the tools to describe uncertainty, simulate it, and understand the stochastic processes behind real computational behavior. Digital Delights makes the ebook convenient to keep, search, and reference alongside your technical library.