MIT OpenCourseWare

Browse materialsMIT 6.262 · Spring 2011

Discrete Stochastic Processes

Subjects: Mathematics · Statistics

Art: not stated, Fletcher's Combined Block Telegraph Apparatus and Bell, 1889 · Public domain

Instructor: Prof. Robert Gallager
Term: Spring 2011
Level: Graduate
What's inside: 25 videos · 94 other files · 5 web pages
Materials open: Videos here · files on MIT OCW

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About this course

Source: MIT OpenCourseWare 6.262 Discrete Stochastic Processes, Spring 2011, Prof. Robert Gallager — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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Course Notes

10 files

Assignments

24 files

Exams

10 files

Other materials from this course

25 files

25 video transcripts

MIT OpenCourseWare, Prof. Robert Gallager, 6.262 Discrete Stochastic Processes, Spring 2011.

License: CC BY-NC-SA 4.0 · Edition: Spring 2011 · Open the original course on OCW ↗

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Discrete Stochastic Processes

Before you start

What's here, and what isn't

Not included here

Materials by section

Course Notes

Assignments

Exams

Other materials from this course

Lecture 1: Introduction and Probability Review

Lecture 2: More Review; The Bernoulli Process

Lecture 3: Law of Large Numbers, Convergence

Lecture 4: Poisson (The Perfect Arrival Process)

Lecture 5: Poisson Combining and Splitting

Lecture 6: From Poisson to Markov

Lecture 7: Finite-state Markov Chains; The Matrix Approach

Lecture 8: Markov Eigenvalues and Eigenvectors

Lecture 9: Markov Rewards and Dynamic Programming

Lecture 10: Renewals and the Strong Law of Large Numbers

Lecture 11: Renewals: Strong Law and Rewards

Lecture 12: Renewal Rewards, Stopping Trials, and Wald's Inequality

Lecture 13: Little, M/G/1, Ensemble Averages

Lecture 14: Review

Lecture 15: The Last Renewal

Lecture 16: Renewals and Countable-state Markov

Lecture 17: Countable-state Markov Chains

Lecture 18: Countable-state Markov Chains and Processes

Lecture 19: Countable-state Markov Processes

Lecture 20: Markov Processes and Random Walks

Lecture 21: Hypothesis Testing and Random Walks

Lecture 22: Random Walks and Thresholds

Lecture 23: Martingales (Plain, Sub, and Super)

Lecture 24: Martingales: Stopping and Converging

Lecture 25: Putting It All Together