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The book gives an accessible account of modern pro- babilistic methods for analyzing combinatorial structures and algorithms. Each topic is approached in a didactic manner but the most recent developments are linked to the basic ma- terial. Extensive lists of references and a detailed index will make this a useful guide for graduate students and researchers. Special features included: - a simple treatment of Talagrand inequalities and their applications- an overview and many carefully worked out examples of the probabilistic analysis of combinatorial algorithms- a discussion of the "exact simulation" algorithm (in the context of Markov Chain Monte Carlo Methods)- a general method for finding asymptotically optimal or near optimal graph colouring, showing how the probabilistic method may be fine-tuned to explit the structure of the underlying graph- a succinct treatment of randomized algorithms and derandomization techniques
