Markov Chains, Probabilistic Models, Text Generation, Hidden Markov Models, Python.
pi[start_state] prev_state = start_state for i in range(1, len(seq)): curr_state = seq[i] prob *= A[prev_state][curr_state] prev_state = curr_state print("Probability: %.4f" % prob) seq_prob([0, 0, 1, 2, 2, 1, 0], A, pi_lev) You can play with the Markov chain by changing parameters. Then, I will