## Presentation Date:

Tuesday, February 16, 2021

## Presentation Slides:

Abstract: Hidden Markov Model is a stochastic model that is characterized by first order Markovian transition between unobserved (hidden) states. If transitions between states are allowed to depend on an external variables, then the homogeneity of the model is relaxed and becomes a non-homogeneous hidden marking model. This model is used ti answer questions such as what is the likelihood of sequence of observations to happen given known model, what the optimal hidden states sequence is, and what is the optimal model that maximized the probability of the observations. Brief examples of HMM libraries in Matlab and Python are provided.