Kurowicka, Harry Joe. Journal of Multivariate Analysis. 100.
   10.1016/j.jmva.2009.04.008
arg_constraints = {'concentration': GreaterThan(lower_bound=0.0)}
expand(batch_shape, _instance=None)
log_prob(value)
sample(sample_shape=torch.Size([]))
support = CorrCholesky()
Laplace
class torch.distributions.laplace.Laplace(loc, scale, validate_args=None)
Bases: "Distribution"
Creates a Laplace distribution parameterized by "loc" and "scale".
Example:
  >>> m = Laplace(torch.tensor([0.0]), torch.tensor([1.0]))
  >>> m.sample()  # Laplace distributed with loc=0, scale=1
  tensor([ 0.1046])

Parameters:
      * loc (float or Tensor) -- mean of the distribution
  * **scale** (*float** or **Tensor*) -- scale of the distribution

arg_constraints = {'loc': Real(), 'scale': GreaterThan(lower_bound=0.0)}
cdf(value)
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
icdf(value)
log_prob(value)