| Summary: | Provides a clear and concise explanation of stochastic theory with an emphasis on computation and examples. This practical graduate text develops theory at an understandable technical level and offers challenging examples to underscore theoretical principles. A special section at the end of each chapter offers additional examples and applications to reinforce the theory in the chapter exercises, and the self-contained chapter on measure theory can be used as a short course or integrated into a general stochastic processes class. The sample course outlines that illustrate the different types of courses that could be based on the text will be of particular interest to professors/instructors.
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