Initial Emission and transition probability

I want to train a HMM classifier with features as input. Considering two observation states(o1, o2) and two hidden states(h1, h2), and some initial probability I apply a supervised algorithm and on the basis of the classifier output, calculate the following Transition prob : [ P(h1/h1), P( h1/ h2); P(h2/ h1),P(h2/h2)]. emission prob: [p(o1/h1), p(o1/h2); p(o2/h1), p(o2/h2)] Is this the correct way to calculate the probabilities?

Answer 1

[Akash]

Hi Pallavi,

I understand that you want to train a "Hidden Markov Model (HMM)" classifier using features as input. You are applying a supervised algorithm and based on the classifier output, you want to calculate the transition probabilities and emission probabilities.

To calculate these probabilities, I recommend referring to the documentation on "Hidden Markov Models (HMM)" in MATLAB. Specifically, you can explore the section "Estimating Transition and Emission Matrices" in the documentation, which provides insights on how to estimate these probabilities in an "HMM". You can find the documentation at the below provided link

Hidden Markov Models (HMM) - MATLAB & Simulink - MathWorks India

Thanks,

Akash.