Video Lectures Discrete Stochastic Processes can view in here
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 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 24: Martingales: Stopping and Converging
Lecture 25: Putting It All Together