Estimate Hidden Markov Processes from Data
Estimate a two-state hidden Markov process with three possible emission values from the given data.
| In[1]:= |  X |
| In[2]:= |  X |
| Out[2]= |  |
Compute the log‐likelihood for the data under the estimated process.
| In[3]:= |  X |
| Out[3]= |  |
Estimate a two-state process with continuous emissions.
| In[4]:= |  X |
The overlaid histograms for each path suggest Gaussian emissions.
| In[5]:= |  X |
| Out[5]= |  |
Compare the results from the default Baum–Welch method and Viterbi training.
| In[6]:= |  X |
| Out[6]= |  |
| In[7]:= |  X |
| Out[7]= |  |
The data has higher log‐likelihood with the Baum–Welch estimated process.
| In[8]:= |  X |
| Out[8]= |  |
