def prior_rev(C, alpha=-1.0):
    r"""Prior counts for sampling of reversible transition
    matrices.

    Prior is defined as

    b_ij= alpha if i<=j
    b_ij=0         else

    Parameters
    ----------
    C : (M, M) ndarray or scipy.sparse matrix
        Count matrix
    alpha : float (optional)
        Value of prior counts

    Returns
    -------
    B : (M, M) ndarray
        Matrix of prior counts

    Notes
    -----
    The reversible prior is a matrix with -1 on the upper triangle.
    Adding this prior respects the fact that
    for a reversible transition matrix the degrees of freedom
    correspond essentially to the upper triangular part of the matrix.

    The prior is defined as

    .. math:: b_{ij} = \left \{ \begin{array}{rl}
                       \alpha & i \leq j \\
                       0      & \text{elsewhere}
                       \end{array} \right .

    Examples
    --------

    >>> import numpy as np
    >>> from msmtools.estimation import prior_rev

    >>> C = np.array([[10, 1, 0], [2, 0, 3], [0, 1, 4]])
    >>> B = prior_rev(C)
    >>> B
    array([[-1., -1., -1.],
           [ 0., -1., -1.],
           [ 0.,  0., -1.]])

    """
    if isdense(C):
        return sparse.prior.prior_rev(C, alpha=alpha)
    else:
        warnings.warn("Prior will be a dense matrix for sparse input")
        return sparse.prior.prior_rev(C, alpha=alpha)
r"""Prior counts for sampling of reversible transition
    matrices.

    Prior is defined as

    b_ij= alpha if i<=j
    b_ij=0         else

    Parameters
    ----------
    C : (M, M) ndarray or scipy.sparse matrix
        Count matrix
    alpha : float (optional)
        Value of prior counts

    Returns
    -------
    B : (M, M) ndarray
        Matrix of prior counts

    Notes
    -----
    The reversible prior is a matrix with -1 on the upper triangle.
    Adding this prior respects the fact that
    for a reversible transition matrix the degrees of freedom
    correspond essentially to the upper triangular part of the matrix.

    The prior is defined as

    .. math:: b_{ij} = \left \{ \begin{array}{rl}
                       \alpha & i \leq j \\
                       0      & \text{elsewhere}
                       \end{array} \right .

    Examples
    --------

    >>> import numpy as np
    >>> from msmtools.estimation import prior_rev

    >>> C = np.array([[10, 1, 0], [2, 0, 3], [0, 1, 4]])
    >>> B = prior_rev(C)
    >>> B
    array([[-1., -1., -1.],
           [ 0., -1., -1.],
           [ 0.,  0., -1.]])