docs: def prior_const(C, alpha=0.001):
      r"""Constant prior for given count matrix.
  
      Parameters
      ----------
      C : (M, M) ndarray or scipy.sparse matrix
          Count matrix
      alpha : float (optional)
          Value of prior counts
  
      Returns
      -------
      B : (M, M) ndarray
          Prior count matrix
  
      Notes
      -----
      The prior is defined as
  
      .. math:: \begin{array}{rl} b_{ij}= \alpha & \forall i, j \end{array}
  
      Examples
      --------
  
      >>> import numpy as np
      >>> from msmtools.estimation import prior_const
  
      >>> C = np.array([[10, 1, 0], [2, 0, 3], [0, 1, 4]])
      >>> B = prior_const(C)
      >>> B
      array([[ 0.001,  0.001,  0.001],
             [ 0.001,  0.001,  0.001],
             [ 0.001,  0.001,  0.001]])
  
      """
      if isdense(C):
          return sparse.prior.prior_const(C, alpha=alpha)
      else:
          warnings.warn("Prior will be a dense matrix for sparse input")
          return sparse.prior.prior_const(C, alpha=alpha)