property variance
Wishart
class torch.distributions.wishart.Wishart(df, covariance_matrix=None, precision_matrix=None, scale_tril=None, validate_args=None)
Bases: "ExponentialFamily"
Creates a Wishart distribution parameterized by a symmetric
   positive definite matrix \Sigma, or its Cholesky decomposition
   \mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top
-[ Example ]-



m = Wishart(torch.eye(2), torch.Tensor([2]))
m.sample()  # Wishart distributed with mean=df * I and
            # variance(x_ij)=df for i != j and variance(x_ij)=2 * df for i == j



Parameters:
      * covariance_matrix (Tensor) -- positive-definite
        covariance matrix
  * **precision_matrix** (*Tensor*) -- positive-definite precision
    matrix

  * **scale_tril** (*Tensor*) -- lower-triangular factor of
    covariance, with positive-valued diagonal

  * **df** (*float** or **Tensor*) -- real-valued parameter larger
