Weibull
class torch.distributions.weibull.Weibull(scale, concentration, validate_args=None)
Bases: "TransformedDistribution"
Samples from a two-parameter Weibull distribution.
-[ Example ]-



m = Weibull(torch.tensor([1.0]), torch.tensor([1.0]))
m.sample()  # sample from a Weibull distribution with scale=1, concentration=1
   tensor([ 0.4784])



Parameters:
      * scale (float or Tensor) -- Scale parameter of
        distribution (lambda).
  * **concentration** (*float** or **Tensor*) -- Concentration
    parameter of distribution (k/shape).

arg_constraints: Dict[str, constraints.Constraint] = {'concentration': GreaterThan(lower_bound=0.0), 'scale': GreaterThan(lower_bound=0.0)}
entropy()
expand(batch_shape, _instance=None)
property mean
property mode
support = GreaterThan(lower_bound=0.0)
property variance
Wishart