1. 2. 3. 4. Training of Discrete Models Turn-in Your Python code: def Baum Welch(N,A,B,Pi,T) : Where: N: Number of model states A: Transition probability matrix B: Output probability matrix T: Training data Pi: Initial state probability matrix Return re-estimated A, B and Pi matrices return [A,B,Pi] # Trained Model 1. 2. 3. Notes: Your code must be original work – That is: it must not be a copy of code found online. Submit your python code in a separate file named “viterbi_.py" As Outlook filters out executable files, submit your work in a .zip file A write-up describing the Baum-Welch algorithm and your implementation of it Note: Be sure to list all references Your hand calculations for the first observation vector A sample run for each of the three observation vectors Note: Your sample run for the 1st two observation vectors must match your hand calculations.

1. 2. 3. 4. Training of Discrete Models Turn-in Your Python code: def Baum Welch(N,A,B,Pi,T) : Where: N: Number of model states A: Transition probability matrix B: Output probability matrix T: Training data Pi: Initial state probability matrix Return re-estimated A, B and Pi matrices return [A,B,Pi] # Trained Model 1. 2. 3. Notes: Your code must be original work – That is: it must not be a copy of code found online. Submit your python code in a separate file named “viterbi_.py" As Outlook filters out executable files, submit your work in a .zip file A write-up describing the Baum-Welch algorithm and your implementation of it Note: Be sure to list all references Your hand calculations for the first observation vector A sample run for each of the three observation vectors Note: Your sample run for the 1st two observation vectors must match your hand calculations.