def tmatrix_cov(C, k=None):
    r"""Covariance tensor for non-reversible transition matrix posterior.

    Parameters
    ----------
    C : (M, M) ndarray or scipy.sparse matrix
        Count matrix
    k : int (optional)
        Return only covariance matrix for entires in the k-th row of
        the transition matrix

    Returns
    -------
    cov : (M, M, M) ndarray
        Covariance tensor for transition matrix posterior

    Notes
    -----
    The posterior of non-reversible transition matrices is

    .. math:: \mathbb{P}(T|C) \propto \prod_{i=1}^{M} \left( \prod_{j=1}^{M} p_{ij}^{c_{ij}} \right)

    Each row in the transition matrix is distributed according to a
    Dirichlet distribution with parameters given by the observed
    transition counts :math:`c_{ij}`.

    The covariance tensor
    :math:`\text{cov}[p_{ij},p_{kl}]=\Sigma_{i,j,k,l}` is zero
    whenever :math:`i \neq k` so that only :math:`\Sigma_{i,j,i,l}` is
    returned.

    """
    if issparse(C):
        warnings.warn("Covariance matrix will be dense for sparse input")
        C = C.toarray()
    return dense.covariance.tmatrix_cov(C, row=k)
def tmatrix_cov ( C , k = None ) : if issparse ( C ) : warnings . warn ( "Covariance matrix will be dense for sparse input" ) C = C . toarray ( ) return dense . covariance . tmatrix_cov ( C , row = k )
r"""Covariance tensor for non-reversible transition matrix posterior.

    Parameters
    ----------
    C : (M, M) ndarray or scipy.sparse matrix
        Count matrix
    k : int (optional)
        Return only covariance matrix for entires in the k-th row of
        the transition matrix

    Returns
    -------
    cov : (M, M, M) ndarray
        Covariance tensor for transition matrix posterior

    Notes
    -----
    The posterior of non-reversible transition matrices is

    .. math:: \mathbb{P}(T|C) \propto \prod_{i=1}^{M} \left( \prod_{j=1}^{M} p_{ij}^{c_{ij}} \right)

    Each row in the transition matrix is distributed according to a
    Dirichlet distribution with parameters given by the observed
    transition counts :math:`c_{ij}`.

    The covariance tensor
    :math:`\text{cov}[p_{ij},p_{kl}]=\Sigma_{i,j,k,l}` is zero
    whenever :math:`i \neq k` so that only :math:`\Sigma_{i,j,i,l}` is
    returned.