Probability - Tijms, Henk
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Probability has applications in many areas of modern science, not to mention in our daily life. Its importance as a mathematical discipline cannot be overrated, and it is a fascinating and surprising topic in its own right. This engaging textbook with its easy-to-follow writing style provides a comprehensive yet concise introduction to the subject. It covers all of the standard material for undergraduate and first-year-graduate-level courses as well as many topics that are usually not found in standard texts, such as Bayesian inference, Markov chain Monte Carlo simulation, and Chernoff bounds.

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Produktbeschreibung
Probability has applications in many areas of modern science, not to mention in our daily life. Its importance as a mathematical discipline cannot be overrated, and it is a fascinating and surprising topic in its own right. This engaging textbook with its easy-to-follow writing style provides a comprehensive yet concise introduction to the subject. It covers all of the standard material for undergraduate and first-year-graduate-level courses as well as many topics that are usually not found in standard texts, such as Bayesian inference, Markov chain Monte Carlo simulation, and Chernoff bounds.
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Autorenporträt
Henk Tijms is emeritus professor at the Vrije University, Amsterdam. He is the author of several textbooks and numerous papers on applied probability and stochastic optimization. In 2008, Henk Tijms received the prestigious INFORMS Expository Writing Award for his publications and books. His activities also include the popularization of probability to high school students and the general public; he also regularly contributed to the Numberplay blog of the New York Times with probability puzzles.
Rezensionen
'This is an attractive textbook for an introductory probability course at the upper undergraduate level. It covers not only the standard material for such a course (discrete probability, the axioms of probability, conditional probability, discrete and continuous random variables, jointly distributed random variables, limit theorems, Markov chains, etc.) but also some topics that might be considered more unusual, such as Kelly betting, renewal-reward stochastic processes, and the law of iterated logarithms. Topics from statistics (confidence intervals, Student-t distribution, Baysian inference, etc.) also appear. The book is quite well-written, nicely motivated, demonstrates considerable enthusiasm for the material, and gives lots of examples of the usefulness of probability. Mark Hunacek, MAA Reviews