class tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor A StochasticTensor with an observed value.

tf.contrib.distributions.WishartCholesky.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Beta.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.framework.create_global_step(graph=None) Create global step tensor in graph. Args: graph: The graph in which to create the global step. If missing, use default graph. Returns: Global step tensor. Raises: ValueError: if global step key is already defined.

tf.contrib.learn.monitors.ValidationMonitor.every_n_step_end(step, outputs)

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

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.distributions.Chi2.std(name='std') Standard deviation.

tensorflow::Status::Status() Create a success status.

tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.