Absorbing and Non-Absorbing Markov Chain 
Absorbing and Non-Absorbing Markov Chain
by iLecturesOnline
Video Lecture 23 of 38
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Date Added: May 21, 2016

Lecture Description

In this video I will explain the difference between an absorbing and non-absorbing Markov chain.

Next video in the Markov Chains series:
youtu.be/UuZU3LUBalQ

Course Index

  1. What are Markov Chains: An Introduction
  2. Markov Chains: An Introduction (Another Method)
  3. Why Are Markov Chains Called "Markov Chains"?
  4. Another Way to Calculate the Markov Chains
  5. What Happens if the Markov Chain Continues?
  6. Markov Chain Applied to Market Penetration
  7. Power of the Probability Matrix
  8. What is a Stochastic Matrix?
  9. What is a Regular Matrix?
  10. Regular Markov Chain
  11. How to Check for a Stable Distribution Matrix
  12. How to Find a Stable 2x2 Matrix - Ex. 1
  13. How to Find a Stable 2x2 Matrix - Ex. 2
  14. How to Find a Stable 2x2 Matrix - Ex. 3
  15. How to Find a Stable 3x3 Matrix
  16. Application Problem #1, Charity Contributions
  17. Application Problem #2, Grocery Stores
  18. Application Problem #3, Brand Loyalty
  19. Absorbing Markov Chains - Definition 1
  20. Absorbing Markov Chains - Definition 2
  21. Absorbing Markov Chains - Example 1
  22. Absorbing Markov Chains - Example 2
  23. Absorbing and Non-Absorbing Markov Chain
  24. Absorbing Markov Chain in Standard Form
  25. Absorbing Markov Chain: Stable Matrix=?
  26. Absorbing Markov Chain: Stable Matrix=? Ex. 1
  27. Absorbing Markov Chain: Stable Matrix=? Ex. 2
  28. Absorbing Markov Chain: Stable Distribution Matrix I
  29. Absorbing Markov Chain: Stable Distribution Matrix II
  30. Basics of Solving Markov Chains
  31. Powers of a Transition Matrix
  32. Finding Stable State Matrix
  33. What is an Absorbing Markov Chain
  34. Finding the Stable State Matrix
  35. Finding the Stable State & Transition Matrices
  36. Absorbing Markov Chain: Standard Form - Ex.
  37. Absorbing Markov Chain: Changing to Standard Form
  38. Absorbing Markov Chain: Standard Form - Ex.

Course Description

In this third and final series on Probability and Statistics, Michel van Biezen introduces Markov chains and stochastic processes and how it predicts the probability of future outcomes.

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