6.262 Discrete Stochastic Processes
25 Videos · Jan 15, 2011
About
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.
Videos
Lecture 18: Countable-state Markov Chains and Processes
Feb 11, 2013
·
2282 views
Lecture 9: Markov Rewards and Dynamic Programming
Feb 11, 2013
·
2400 views
Lecture 14: Review
Feb 11, 2013
·
1920 views
Lecture 7: Finite-state Markov Chains; The Matrix Approach
Feb 11, 2013
·
3443 views
Lecture 5: Poisson Combining and Splitting
Feb 11, 2013
·
2272 views
Lecture 4: Poisson (The Perfect Arrival Process)
Feb 11, 2013
·
2392 views
Lecture 15: The Last Renewal
Feb 11, 2013
·
2086 views
Lecture 16: Renewals and Countable-state Markov
Feb 11, 2013
·
2103 views
Lecture 23: Martingales (Plain, Sub, and Super)
Feb 11, 2013
·
2250 views
Lecture 12: Renewal Rewards, Stopping Trials, and Wald's Inequality
Feb 11, 2013
·
2121 views
Lecture 21: Hypothesis Testing and Random Walks
Feb 11, 2013
·
2448 views
Lecture 13: Little, M/G/1, Ensemble Averages
Feb 11, 2013
·
2244 views
Lecture 2: More Review; The Bernoulli Process
Feb 11, 2013
·
3177 views
Lecture 3: Law of Large Numbers, Convergence
Feb 11, 2013
·
2333 views
Lecture 20: Markov Processes and Random Walks
Feb 11, 2013
·
2232 views
Lecture 19: Countable-state Markov Processes
Feb 11, 2013
·
2053 views
Lecture 25: Putting It All Together
Feb 11, 2013
·
2133 views
Lecture 10: Renewals and the Strong Law of Large Numbers
Feb 11, 2013
·
2231 views
Lecture 24: Martingales: Stopping and Converging
Feb 11, 2013
·
2318 views
Lecture 17: Countable-state Markov Chains
Feb 11, 2013
·
2039 views
Lecture 8: Markov Eigenvalues and Eigenvectors
Feb 11, 2013
·
2412 views
Lecture 6: From Poisson to Markov
Feb 11, 2013
·
3899 views
Lecture 11: Renewals: Strong Law and Rewards
Feb 11, 2013
·
2106 views
Lecture 1: Introduction to Discrete Stochastic Processes and Probability Review
Feb 11, 2013
·
7525 views
Lecture 22: Random Walks and Thresholds
Feb 11, 2013
·
2003 views