tf.contrib.distributions.Dirichlet.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.distributions.StudentT.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.distributions.Normal.name Name prepended to all ops created by this Distribution.

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

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

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.dtype

tf.assert_proper_iterable(values) Static assert that values is a "proper" iterable. Ops that expect iterables of Tensor can call this to validate input. Useful since Tensor, ndarray, byte/text type are all iterables themselves. Args: values: Object to be checked. Raises: TypeError: If values is not iterable or is one of Tensor, SparseTensor, np.array, tf.compat.bytes_or_text_types.

TTypes< T, NDIMS >::Tensor tensorflow::Tensor::flat_outer_dims() Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing all Tensor dimensions but the first NDIMS-1 into the last dimension of the result. If NDIMS > dims() then trailing dimensions of size 1 will be added to make the output rank NDIMS.

bool tensorflow::Tensor::SharesBufferWith(const Tensor &b) const

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor MultivariateNormalDiagPlusVDVTTensor is a StochasticTensor backed by the distribution MultivariateNormalDiagPlusVDVT.