Markov Chains, Markov Process, Python, Discrete Time Markov, Probability.
pi.append(pi_0) P_mul=P.copy() #calculate the state probability for each t and store it away for i in range(10): P_mul=np.matmul(P_mul,P) pi_t = np.matmul(pi_0,P_mul) pi.append(pi_t) pi = np.array(pi) The above code stores all the calculated π_t vectors in the array π. Let’s separately plot the