Probability and Random Processes Advanced Topics

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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.

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What Will You Learn?

  • Learn the Advanced Topics in Probability and Random Processes

Course Content

Advanced Topics in Probability and Random Processes

  • Advanced Topics in Probability and Random Processes
    00:00
  • 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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