Markov Chains, Probabilistic Models, Text Generation, Hidden Markov Models, Python.
= start print(states[start_state], end=" ") prev_state = start_state while(n_steps>1): curr_state = np.random.choice([0, 1, 2], p=A[prev_state]) print(">>>", states[curr_state], end=" ") prev_state = curr_state n_steps -= 1 print("\nRandom walk is done!") random_walk(10, 0) To find the stationary