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Markov Chains

Part 4: Summary

Definitions:

We saw two ways that we can try to find a steady-state vector:

  1. We can compute the limiting value of a matrix expression. In your worksheet, describe that process and how to interpret the results.
  2. We can solve a matrix equation. Write the equation in your worksheet. What is the side condition that makes the solution unique?

We studied three examples in this module. In two of the examples, the Markov chain converged to the steady-state vector. In the third example, we saw a Markov chain that did not converge. However, the Markov matrix did have a steady-state vector.

  1. What characteristic of the third transition matrix made this Markov chain behave differently from the first two?

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