


m.sample()  # Fisher-Snedecor-distributed with df1=1 and df2=2
      tensor([ 0.2453])



Parameters:
      * df1 (float or Tensor) -- degrees of freedom
        parameter 1
  * **df2** (*float** or **Tensor*) -- degrees of freedom
    parameter 2

arg_constraints = {'df1': GreaterThan(lower_bound=0.0), 'df2': GreaterThan(lower_bound=0.0)}
expand(batch_shape, _instance=None)
has_rsample = True
log_prob(value)
property mean
property mode
rsample(sample_shape=torch.Size([]))
support = GreaterThan(lower_bound=0.0)
property variance
Gamma
class torch.distributions.gamma.Gamma(concentration, rate, validate_args=None)
Bases: "ExponentialFamily"
Creates a Gamma distribution parameterized by shape "concentration"
   and "rate".
Example:
  >>> m = Gamma(torch.tensor([1.0]), torch.tensor([1.0]))
  >>> m.sample()  # Gamma distributed with concentration=1 and rate=1
  tensor([ 0.1046])

Parameters: