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

static bool tensorflow::TensorShapeUtils::IsVector(const TensorShape &shape)

tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.name

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.Poisson.is_reparameterized

tf.contrib.distributions.Dirichlet.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.value_type

const TensorShape& tensorflow::Tensor::shape() const Returns the shape of the tensor.

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