Algorithms, Dynamic Programming, Machine Learning, Artificial Intelligence, Markov Models.
the first time step. path_probabilities.append({ possible_state: PathProbabilityWithBackPointer( hmm.p[possible_state] * hmm.b[possible_state][observations[0]]) for possible_state in hmm.s }) Next comes the main loop, where we calculate V(t, s) for every possible state s in terms of V(t−1, r) for