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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 [course_title] 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

Course Curriculum

Module: 1
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
Module: 2
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
Module: 3
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
Module: 4
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
Module: 5
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
Certification 00:00:00

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