tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.graph

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.graph

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

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

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False) Sample n times and concatenate

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.input_dict