property variance
Dirichlet
class torch.distributions.dirichlet.Dirichlet(concentration, validate_args=None)
Bases: "ExponentialFamily"
Creates a Dirichlet distribution parameterized by concentration
   "concentration".
Example:
  >>> m = Dirichlet(torch.tensor([0.5, 0.5]))
  >>> m.sample()  # Dirichlet distributed with concentration [0.5, 0.5]
  tensor([ 0.1046,  0.8954])

Parameters:
      concentration (Tensor) -- concentration parameter of the
      distribution (often referred to as alpha)
arg_constraints = {'concentration': IndependentConstraint(GreaterThan(lower_bound=0.0), 1)}
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
log_prob(value)
property mean
property mode
rsample(sample_shape=())
support = Simplex()
property variance
Exponential
class torch.distributions.exponential.Exponential(rate, validate_args=None)
Bases: "ExponentialFamily"