An update of this popular introduction to probability theory and information theory with new material on Markov chains.This updated, popular introduction to probability theory and information theory, only requiring knowledge of basic calculus, contains new material on Markov chains. Topics covered include statistical inference, random walks, statistical mechanics and communications modelling. Examples and exercises illustrate practical applications with detailed solutions to most exercises available online for instructors.This updated, popular introduction to probability theory and information theory, only requiring knowledge of basic calculus, contains new material on Markov chains. Topics covered include statistical inference, random walks, statistical mechanics and communications modelling. Examples and exercises illustrate practical applications with detailed solutions to most exercises available online for instructors.This new and updated textbook is an excellent way to introduce probability and information theory to students new to mathematics, computer science, engineering, statistics, economics, or business studies. Only requiring knowledge of basic calculus, it begins by building a clear and systematic foundation to probability and information. Classic topics covered include discrete and continuous random variables, entropy and mutual information, maximum entropy methods, the central limit theorem and the coding and transmission of information. Newly covered for this edition is modern material on Markov chains and their entropy. Examples and exercises are included to illustrate how to use the theory in a wide range of applications, with detailed solutions to most exercises available online for instructors.Preface to the first edition; Preface to the second edition; 1. Introduction; 2. Combinatorics; 3. Sets and measures; 4. Probability; 5. Discrete random variables; 6. Information and entropy; 7. Communication; 8. Random variables with probability density flҼ