def stationary_distribution(T):
    r"""Compute stationary distribution of stochastic matrix T.

    Chooses the fastest applicable algorithm automatically

    Input:
    ------
    T : numpy array, shape(d,d)
        Transition matrix (stochastic matrix).

    Returns
    -------
    mu : numpy array, shape(d,)
        Vector of stationary probabilities.

    """
    fallback = False
    try:
        mu = stationary_distribution_from_backward_iteration(T)
        if np.any(mu < 0):  # numerical problem, fall back to more robust algorithm.
            fallback=True
    except RuntimeError:
        fallback = True

    if fallback:
        mu = stationary_distribution_from_eigenvector(T)
        if np.any(mu < 0):  # still? Then set to 0 and renormalize
            mu = np.maximum(mu, 0.0)
            mu /= mu.sum()

    return mu
r"""Compute stationary distribution of stochastic matrix T.

    Chooses the fastest applicable algorithm automatically

    Input:
    ------
    T : numpy array, shape(d,d)
        Transition matrix (stochastic matrix).

    Returns
    -------
    mu : numpy array, shape(d,)
        Vector of stationary probabilities.