Computing Markov Stationary Distributions in mc_tools.py

[cc7768]

There can be multiple stationary distributions (different absorbing states for example) in a discrete markov transition matrix. The current code in mc_tools/mc_compute_stationary.py cannot handle this example. We can solve this using eigenvalues instead and this possibly could solve #18 as well. Creating a separate branch and will update on progress.

import numpy as np
import quantecon as qe

P = np.array([[1, 0], [0, 1]])

qe.mc_tools.mc_compute_stationary(P)

>>> LinAlgError: Singular matrix

[cc7768]

Fixed with #43