About Course
The Probability and Random Processes course will cover mainly two broad areas:
(1) the concepts of the convergence of a sequence of random variables leading to the explanation of important concepts like the laws of large numbers, central limit theorem; and (2) Markov chains that include the analysis of discrete and continuous-time Markov Chains and their applications.
Course Content
Advanced Topics in Probability and Random Processes
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Advanced Topics in Probability and Random Processes
05:28 -
Lec 1: Probability Basics
00:00 -
Lec 2: Random Variable-I
00:00 -
Lec 3: Random Variable-II
00:00 -
Lec 4: Random Vectors and Random Processes
00:00 -
Lec 5: Infinite Sequence of Events-l
00:00 -
Lec 6: Infinite Sequence of Events-ll
00:00 -
Lec 7: Convergence of Sequence of Random Variables
00:00 -
Lec 8: Weak Convergence-I
00:00 -
Lec 9: Weak Convergence-II
00:00 -
Lec 10: Laws of Large Numbers
00:00 -
Lec 11: Central Limit Theorem
00:00 -
Lec 12: Large Deviation Theory
00:00 -
Lec 13: Crammer’s Theorem for Large Deviation
00:00 -
Lec 14: Introduction to Markov Processes
00:00 -
Lec 15: Discrete Time Markov Chain
00:00 -
Lec 16: Discrete Time Markov Chain-2
00:00 -
Lec 17: Discrete Time Markov Chain-3
00:00 -
Lec 18: Discrete Time Markov Chain-4
00:00 -
Lec 19: Discrete Time Markov Chain-5
00:00 -
Lec 20: Continuous Time Markov Chain – 1
00:00 -
Lec 21: Continuous Time Markov Chain – 2
00:00 -
Lec 22: Continuous Time Markov Chain – 3
00:00 -
Lec 23: Martingle Process-1
00:00 -
Lec 24: Martingle Process-2
00:00
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