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

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

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

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function') Log survival function

tf.matching_files(pattern, name=None) Returns the set of files matching a pattern. Note that this

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.entropy(name='entropy')

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

tf.contrib.distributions.Multinomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Multinomial') Initialize

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.distributions.Exponential.allow_nan_stats Python boolean describing behavior when a stat is undefined.