6.262 Discrete Stochastic Processes
25 Lectures · 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.
Related categories
Uploaded videos:
Lecture 1: Introduction to Discrete Stochastic Processes and Probability Review
Feb 11, 2013
·
7517 Views
Lecture 2: More Review; The Bernoulli Process
Feb 11, 2013
·
3174 Views
Lecture 3: Law of Large Numbers, Convergence
Feb 11, 2013
·
2327 Views
Lecture 4: Poisson (The Perfect Arrival Process)
Feb 11, 2013
·
2387 Views
Lecture 5: Poisson Combining and Splitting
Feb 11, 2013
·
2265 Views
Lecture 6: From Poisson to Markov
Feb 11, 2013
·
3895 Views
Lecture 7: Finite-state Markov Chains; The Matrix Approach
Feb 11, 2013
·
3436 Views
Lecture 8: Markov Eigenvalues and Eigenvectors
Feb 11, 2013
·
2406 Views
Lecture 9: Markov Rewards and Dynamic Programming
Feb 11, 2013
·
2394 Views
Lecture 10: Renewals and the Strong Law of Large Numbers
Feb 11, 2013
·
2228 Views
Lecture 11: Renewals: Strong Law and Rewards
Feb 11, 2013
·
2102 Views
Lecture 12: Renewal Rewards, Stopping Trials, and Wald's Inequality
Feb 11, 2013
·
2117 Views
Lecture 18: Countable-state Markov Chains and Processes
Feb 11, 2013
·
2277 Views
Lecture 13: Little, M/G/1, Ensemble Averages
Feb 11, 2013
·
2241 Views
Lecture 14: Review
Feb 11, 2013
·
1917 Views
Lecture 15: The Last Renewal
Feb 11, 2013
·
2083 Views
Lecture 16: Renewals and Countable-state Markov
Feb 11, 2013
·
2099 Views
Lecture 17: Countable-state Markov Chains
Feb 11, 2013
·
2035 Views
Lecture 19: Countable-state Markov Processes
Feb 11, 2013
·
2048 Views
Lecture 20: Markov Processes and Random Walks
Feb 11, 2013
·
2228 Views
Lecture 21: Hypothesis Testing and Random Walks
Feb 11, 2013
·
2439 Views
Lecture 22: Random Walks and Thresholds
Feb 11, 2013
·
1998 Views
Lecture 23: Martingales (Plain, Sub, and Super)
Feb 11, 2013
·
2245 Views
Lecture 24: Martingales: Stopping and Converging
Feb 11, 2013
·
2314 Views
Lecture 25: Putting It All Together
Feb 11, 2013
·
2129 Views