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
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2293 views

Lecture 9: Markov Rewards and Dynamic Programming
Feb 11, 2013
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2405 views

Lecture 14: Review
Feb 11, 2013
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1930 views

Lecture 7: Finite-state Markov Chains; The Matrix Approach
Feb 11, 2013
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3450 views

Lecture 5: Poisson Combining and Splitting
Feb 11, 2013
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2276 views

Lecture 4: Poisson (The Perfect Arrival Process)
Feb 11, 2013
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2394 views

Lecture 15: The Last Renewal
Feb 11, 2013
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2098 views

Lecture 16: Renewals and Countable-state Markov
Feb 11, 2013
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2114 views

Lecture 23: Martingales (Plain, Sub, and Super)
Feb 11, 2013
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2256 views

Lecture 12: Renewal Rewards, Stopping Trials, and Wald's Inequality
Feb 11, 2013
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2135 views

Lecture 21: Hypothesis Testing and Random Walks
Feb 11, 2013
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2462 views

Lecture 13: Little, M/G/1, Ensemble Averages
Feb 11, 2013
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2252 views

Lecture 2: More Review; The Bernoulli Process
Feb 11, 2013
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3186 views

Lecture 3: Law of Large Numbers, Convergence
Feb 11, 2013
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2340 views

Lecture 20: Markov Processes and Random Walks
Feb 11, 2013
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2241 views

Lecture 19: Countable-state Markov Processes
Feb 11, 2013
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2056 views

Lecture 25: Putting It All Together
Feb 11, 2013
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2139 views

Lecture 10: Renewals and the Strong Law of Large Numbers
Feb 11, 2013
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2242 views

Lecture 24: Martingales: Stopping and Converging
Feb 11, 2013
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2323 views

Lecture 17: Countable-state Markov Chains
Feb 11, 2013
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2046 views

Lecture 8: Markov Eigenvalues and Eigenvectors
Feb 11, 2013
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2424 views

Lecture 6: From Poisson to Markov
Feb 11, 2013
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3904 views

Lecture 11: Renewals: Strong Law and Rewards
Feb 11, 2013
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2112 views

Lecture 1: Introduction to Discrete Stochastic Processes and Probability Review
Feb 11, 2013
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7535 views

Lecture 22: Random Walks and Thresholds
Feb 11, 2013
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2012 views