Algorithms, Dynamic Programming, Machine Learning, Artificial Intelligence, Markov Models.
over all the possible end states at the last time step. max_path_end_state = max( hmm.possible_states, key=lambda s: path_probabilities[-1][s].probability ) Finally, we can now follow the back pointers to reconstruct the most probable path. path = [] path_step_state = max_path_end_state for t in