tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.input_dict

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.is_reparameterized

tf.contrib.distributions.ExponentialWithSoftplusLam.is_continuous

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.mode(name='mode') Mode.

tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n') Generate n samples.

tf.contrib.distributions.Mixture.pdf(value, name='pdf') Probability density function. Args:

tf.contrib.distributions.Poisson.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value(name='value')