Markov Chains, Probabilistic Models, Text Generation, Hidden Markov Models, Python.
= steps A_new = A itr = 0 while(itr<steps): A_new = np.matmul(A_new, A) itr += 1 print("PI = ", A_new[0]) repeated_mat_mul(10 ** 3) As the third, I will use the left eigenvectors approach. # Left Eigen Vectors approach def left_eigen_vectors(): val, left = sp.linalg.eig(A, right = False, left =