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This graduate-level text concentrates on measure theory and modern probability based on measure theory. Its unique feature is the way it intertwines the two subjects: probability problems generate an interest in measure theory and measure theory is then developed and applied to probability. In this new edition, queuing theory is replaced by ergodic theory, modern conditional probability is introduced sooner, and the treatment of Brownian motion is improved. Numerous problems are included in the text.
From the back cover:
Now in its new third edition, Probability and Measure offers advanced students, scientists, and engineers an integrated introduction to measure theory and probability. Retaining the unique approach of the previous editions, this text interweaves material on probability and measure, so that probability problems generate an interest in measure theory and measure theory is then developed and applied to probability. Probability and Measure provides thorough coverage of probability, measure, integration, random variables and expected values, convergence of distributions, derivatives and conditional probability, and stochastic processes. The Third Edition features an improved treatment of Brownian motion and the replacement of queuing theory with ergodic theory.
Like the previous editions, this new edition will be well received by students of mathematics, statistics, economics, and a wide variety of disciplines that require a solid understanding of probability theory.
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