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Probability and Random Processes Advanced Topics

  • Course level: Intermediate

Description

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.

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What Will I Learn?

  • Learn the Advanced Topics in Probability and Random Processes

Topics for this course

25 Lessons

Advanced Topics in Probability and Random Processes

Advanced Topics in Probability and Random Processes00:00:00
Lec 1: Probability Basics00:00:00
Lec 2: Random Variable-I00:00:00
Lec 3: Random Variable-II00:00:00
Lec 4: Random Vectors and Random Processes00:00:00
Lec 5: Infinite Sequence of Events-l00:00:00
Lec 6: Infinite Sequence of Events-ll00:00:00
Lec 7: Convergence of Sequence of Random Variables00:00:00
Lec 8: Weak Convergence-I00:00:00
Lec 9: Weak Convergence-II00:00:00
Lec 10: Laws of Large Numbers00:00:00
Lec 11: Central Limit Theorem00:00:00
Lec 12: Large Deviation Theory00:00:00
Lec 13: Crammer’s Theorem for Large Deviation00:00:00
Lec 14: Introduction to Markov Processes00:00:00
Lec 15: Discrete Time Markov Chain00:00:00
Lec 16: Discrete Time Markov Chain-200:00:00
Lec 17: Discrete Time Markov Chain-300:00:00
Lec 18: Discrete Time Markov Chain-400:00:00
Lec 19: Discrete Time Markov Chain-500:00:00
Lec 20: Continuous Time Markov Chain – 100:00:00
Lec 21: Continuous Time Markov Chain – 200:00:00
Lec 22: Continuous Time Markov Chain – 300:00:00
Lec 23: Martingle Process-100:00:00
Lec 24: Martingle Process-200:00:00
Probability and Random Processes
Free

Enrolment validity: Lifetime

Requirements

  • None