Estimate Hidden Markov Processes from Data 

Estimate a two-state hidden Markov process with three possible emission values from the given data.

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Compute the loglikelihood for the data under the estimated process.

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Estimate a two-state process with continuous emissions.

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The overlaid histograms for each path suggest Gaussian emissions.

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Compare the results from the default BaumWelch method and Viterbi training.

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The data has higher loglikelihood with the BaumWelch estimated process.

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