class_docstr: In its most common representation, a multivariate Gaussian distribution
  over X1, X2, ..., Xn is characterized by an n-dimensional mean vector μ,
  and a symmetric n x n covariance matrix Σ.
  
  This is the base class for its representation.
docstr: 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]])
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code: 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
  
imports: import numpy
  from scipy.stats import multivariate_normal
  from pgmpy.factors.distributions import BaseDistribution
label_desc: Use image node_id 4 for calling the GaussianDistribution obj's underlying member method code with example usage: obj.precision_matrix() and returns: self