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 log‐likelihood for the data under the estimated process.
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Estimate a two-state process with continuous emissions.
The overlaid histograms for each path suggest Gaussian emissions.
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Compare the results from the default Baum–Welch method and Viterbi training.
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The data has higher log‐likelihood with the Baum–Welch estimated process.
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