Stochastic processes are part of the probabilistic systems which involves the time. The discrete stochastic process can only be changed by integer time steps and can be characterized at arbitrary times which are discussed here.
The information you will get from this Stochastic Processes Diploma will essentially help you understand the mathematical principles and importance of intuition. These are used to create, analyze, and understand a broad range of models and processes.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: MIT
|Lecture 1: Introduction and Probability Review||01:16:00|
|Lecture 2: More Review; The Bernoulli Process||01:08:00|
|Lecture 3: Law of Large Numbers, Convergence||01:22:00|
|Lecture 4: Poisson (The Perfect Arrival Process)||01:17:00|
|Lecture 5: Poisson Combining and Splitting||01:25:00|
|Lecture 6: From Poisson to Markov||01:19:00|
|Lecture 7: Finite-state Markov Chains; The Matrix Approach||00:56:00|
|Lecture 8: Markov Eigenvalues and Eigenvectors||01:24:00|
|Lecture 9: Markov Rewards and Dynamic Programming||01:24:00|
|Lecture 10: Renewals and the Strong Law of Large Numbers||01:22:00|
|Lecture 11: Renewals: Strong Law and Rewards||01:18:00|
|Lecture 12: Renewal Rewards, Stopping Trials, and Wald’s Inequality||01:27:00|
|Lecture 13: Little, M/G/1, Ensemble Averages||01:15:00|
|Lecture 14: Review||01:19:00|
|Lecture 15: The Last Renewal||01:16:00|
|Lecture 16: Renewals and Countable-state Markov||01:20:00|
|Lecture 17: Countable-state Markov Chains||01:24:00|
|Lecture 18: Countable-state Markov Chains and Processes||01:17:00|
|Lecture 19: Countable-state Markov Processes||01:22:00|
|Lecture 20: Markov Processes and Random Walks||01:23:00|
|Lecture 21: Hypothesis Testing and Random Walks||01:26:00|
|Lecture 22: Random Walks and Thresholds||01:21:00|
|Lecture 23: Martingales (Plain, Sub, and Super)||01:23:00|
|Lecture 24: Martingales: Stopping and Converging||01:21:00|
|Lecture 25: Putting It All Together||01:22:00|
|Submit Your Assignment||00:00:00|
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