docs: def precision_matrix(self):
          """
          Returns the precision matrix of the distribution.
  
          Precision is defined as the inverse of the variance. This method returns
          the inverse matrix of the covariance.
  
          Examples
          --------
          >>> import numpy as np
          >>> from pgmpy.factors.distributions import GaussianDistribution as GD
          >>> dis = GD(variables=['x1', 'x2', 'x3'],
          ...          mean=[1, -3, 4],
          ...          cov=[[4, 2, -2],
          ...               [2, 5, -5],
          ...               [-2, -5, 8]]))
          >>> dis.precision_matrix
          array([[ 0.3125    , -0.125     ,  0.        ],
                 [-0.125     ,  0.58333333,  0.33333333],
                 [ 0.        ,  0.33333333,  0.33333333]])
          """
          if self._precision_matrix is None:
              self._precision_matrix = np.linalg.inv(self.covariance)
          return self._precision_matrix