Synopsis
Suitable for a graduate course in analytic probability, this text requires only a limited background in real analysis. Topics include probability spaces and distributions, stochastic independence, basic limiting options, strong limit theorems for independent random variables, central limit theorem, conditional expectation and Martingale theory, and an introduction to stochastic processes.
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About the Author
Howard G. Tucker is Professor Emeritus of Mathematics at the University of California, Irvine. His other books include Mathematical Methods for Sample Surveys.
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