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.ExponentialTensor.input_dict

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

tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf') Log probability mass function.

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.value_type

tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape