tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.name

tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='Gamma') Construct Gamma

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.dtype

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.variance(name='variance') Variance.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.input_dict

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

tf.contrib.distributions.Chi2.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired

tf.contrib.distributions.WishartFull.mean(name='mean') Mean.

tf.contrib.distributions.Gamma.std(name='std') Standard deviation.