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Advanced Topics in Probability and Random Processes

Lec 1: Probability Basics

Lec 2: Random Variable-I

Lec 3: Random Variable-II

Lec 4: Random Vectors and Random Processes

Lec 5: Infinite Sequence of Events-l

Lec 6: Infinite Sequence of Events-ll

Lec 7: Convergence of Sequence of Random Variables

Lec 8: Weak Convergence-I

Lec 9: Weak Convergence-II

Lec 10: Laws of Large Numbers

Lec 11: Central Limit Theorem

Lec 12: Large Deviation Theory

Lec 13: Crammer's Theorem for Large Deviation

Lec 14: Introduction to Markov Processes

Lec 15: Discrete Time Markov Chain

Lec 16: Discrete Time Markov Chain-2

Lec 17: Discrete Time Markov Chain-3

Lec 18: Discrete Time Markov Chain-4

Lec 19: Discrete Time Markov Chain-5

Lec 20: Continuous Time Markov Chain - 1

Lec 21: Continuous Time Markov Chain - 2

Lec 22: Continuous Time Markov Chain - 3

Lec 23: Martingle Process-1

Lec 24: Martingle Process-2

Description:

Course Intro: The course will cover mainly two broad areas: (1) the concepts of the convergence 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. Pre Requisites: Basic Course in Probability

Advanced Topics in Probability and Random Processes

NPTEL

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#Engineering #Electrical Engineering #Mathematics #Statistics & Probability #Probability #Central Limit Theorem

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