def binomial(self,
name,
logits,
n_experiments,
n_samples=None,
group_ndims=0,
dtype=tf.int32,
check_numerics=False,
**kwargs):
"""
Add a stochastic node in this :class:`BayesianNet` that follows the
Binomial distribution.
:param name: The name of the stochastic node. Must be unique in a
:class:`BayesianNet`.
See
:class:`~zhusuan.distributions.univariate.Binomial` for more information
about the other arguments.
:return: A :class:`StochasticTensor` instance.
"""
dist = distributions.Binomial(logits,
n_experiments,
group_ndims=group_ndims,
dtype=dtype,
check_numerics=check_numerics,
**kwargs)
return self.stochastic(name, dist, n_samples=n_samples, **kwargs)
def __init__(self,
name,
logits,
n_experiments,
n_samples=None,
group_event_ndims=0,
dtype=None,
check_numerics=False):
binomial = distributions.Binomial(logits,
n_experiments,
group_event_ndims=group_event_ndims,
dtype=dtype,
check_numerics=check_numerics)
super(Binomial, self).__init__(name, binomial, n_samples)