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Hidden Markov Model LR Rabiner

Hidden Markov Model LR Rabiner. 12.04.26.(Thu) Computational Models of Intelligence Joon Shik Kim. Discrete Markov Process. Variables. S: state (rain, cloudy, sunny) O: observation (umbrella) How to infer the weather sequences based on only the observations?. Three Basic Problems for HMM.

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Hidden Markov Model LR Rabiner

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  1. Hidden Markov ModelLR Rabiner 12.04.26.(Thu) Computational Models of Intelligence Joon Shik Kim

  2. Discrete Markov Process

  3. Variables • S: state (rain, cloudy, sunny) • O: observation (umbrella) • How to infer the weather sequences based on only the observations?

  4. Three Basic Problems for HMM • Problem 1: Given the observation sequence O and model , how do we efficiently compute , the probability of the observation sequence, given the model? • Problem 2: Given the observation sequence O and the model , how do we choose a corresponding state sequence Q which is optimal in some meaningful sense (i.e., best “explains” the observations)?

  5. Three Basic Problems for HMM • Problem 3: How do we adjust the model parameters to maximize ?

  6. Solution to Problem 1 Forward-Backward Procedure

  7. Solution to Problem 2 • Viterbi algorithm

  8. Solution to Problem 3 • Baum-Welch method • EM (expectation-modification) method

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