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Discrete Stochastic Processes (Gallager)

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    Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of these processes. The range of areas for which discrete stochastic-process models are useful is constantly expanding, and includes many applications in engineering, physics, biology, operations research and finance.

    Thumbnail: Tandem queues: A stable M/M/1 queue has a Poisson output at the input rate. The next queue also has a Poisson output at that rate. (Image by MIT OpenCourseWare, adapted from Prof. Robert Gallager's course notes.)

    This page titled Discrete Stochastic Processes (Gallager) is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Robert Gallager (MIT OpenCourseWare) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.