2 comments on “Primer on Hidden Markov Models: Part 1

  1. I’ve always found the other method of calculating the steady state probabilities to be pretty interesting also.

    qP = q
    qP = qI
    q(P-I) = 0

    you end up with n simultaneous equations for n variables (plus the constraint that they must sum to 1).

    PS, your sunny/rainy transition probabilities are way off for seattle.

  2. The other method is a lot nicer for getting the exact answer as opposed to an approximation. Raising the matrix to some extreme number is easier to visualize and plug in to MATLAB.

    And you are correct, I wasn’t modeling Seattle. I imagine the probabilities for sunny/rainy would be flipped :P

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