tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.value_type

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

tf.contrib.distributions.MultivariateNormalCholesky.event_shape(name='event_shape') Shape of a single sample from a single batch

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

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

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf') Probability mass function.

tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf') Probability mass function. Args:

tf.contrib.distributions.StudentT.parameters Dictionary of parameters used by this Distribution.

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

tf.contrib.distributions.Exponential.is_continuous