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Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals
FREE
This course includes
Hours of videos
694 years, 4 months
Units & Quizzes
25
Unlimited Lifetime access
Access on mobile app
Certificate of Completion
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.
Course Currilcum
- Introduction and Probability Review Unlimited
- More Review; The Bernoulli Process Unlimited
- Law of Large Numbers, Convergence Unlimited
- Poisson (The Perfect Arrival Process) Unlimited
- Poisson Combining and Splitting Unlimited
- From Poisson to Markov Unlimited
- Finite State Markov Chains; The Matrix Approach Unlimited
- Markov Eigenvalues and Eigenvectors Unlimited
- Markov Rewards and Dynamic Programming Unlimited
- Renewals and The Strong Law of Large Numbers Unlimited
- Renewals; Strong Law and Rewards Unlimited
- Renewal Rewards Stopping Trials and Walds Inequality Unlimited
- Little, M/G/1, Ensemble Averages Unlimited
- Review Unlimited
- The Last Renewal Unlimited
- Renewals and Counta-State Markovble Unlimited
- Countable-State Markov Chains Unlimited
- Countable-State Markov Chains and Processes Unlimited
- Countable-State Markov Processes Unlimited
- Markov Processes and Random Walks Unlimited
- Hypothesis Testing and Random Walks Unlimited
- Random Walks and Thresholds Unlimited
- Martingales (Plain, Sub, and Super) Unlimited
- Martingales: Stopping and Converging Unlimited
- Putting it all Together Unlimited
