MIT 6.262 Discrete Stochastic Processes, Spring 2011

Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. Created by MIT OpenCourseWare.

Average Course Length

40 hours

Skill Level


Pick a lesson

1: Introduction and Probability Review
2: More Review; The Bernoulli Process
3: Law of Large Numbers, Convergence
4: Poisson (the Perfect Arrival Process)
5: Poisson Combining and Splitting
6: From Poisson to Markov
7: Finite-state Markov Chains; The Matrix Approach
8: Markov Eigenvalues and Eigenvectors
9: Markov Rewards and Dynamic Programming
10: Renewals and the Strong Law of Large Numbers
11: Renewals: Strong Law and Rewards
12: Renewal Rewards, Stopping Trials, and Wald's Inequality
13: Little, M/G/1, Ensemble Averages
14: Review
15: The Last Renewal
16: Renewals and Countable-state Markov
17: Countable-state Markov Chains
18: Countable-state Markov Chains and Processes
19: Countable-state Markov Processes
20: Markov Processes and Random Walks
21: Hypothesis Testing and Random Walks
22: Random Walks and Thresholds
23: Martingales (Plain, Sub, and Super)
24: Martingales: Stopping and Converging
25: Putting It All Together