def inverse_gamma(self,
name,
alpha,
beta,
n_samples=None,
group_ndims=0,
check_numerics=False,
**kwargs):
"""
Add a stochastic node in this :class:`BayesianNet` that follows the
InverseGamma distribution.
:param name: The name of the stochastic node. Must be unique in a
:class:`BayesianNet`.
See
:class:`~zhusuan.distributions.univariate.InverseGamma`
for more information about the other arguments.
:return: A :class:`StochasticTensor` instance.
"""
dist = distributions.InverseGamma(alpha,
beta,
group_ndims=group_ndims,
check_numerics=check_numerics,
**kwargs)
return self.stochastic(name, dist, n_samples=n_samples, **kwargs)
def __init__(self,
name,
alpha,
beta,
n_samples=None,
group_ndims=0,
check_numerics=False,
**kwargs):
inv_gamma = distributions.InverseGamma(alpha,
beta,
group_ndims=group_ndims,
check_numerics=check_numerics,
**kwargs)
super(InverseGamma, self).__init__(name, inv_gamma, n_samples)