tf.contrib.graph_editor.ControlOutputs.update() Update the control outputs if the graph has changed.

tf.contrib.distributions.Dirichlet.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.bayesflow.stochastic_tensor.SampleValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)

tf.contrib.graph_editor.OpMatcher.output_ops(*args) Add output matches.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.distribution

tf.contrib.distributions.WishartCholesky.mode(name='mode') Mode.

tf.contrib.graph_editor.OpMatcher.control_input_ops(*args) Add input matches.

tf.contrib.distributions.WishartCholesky.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

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

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value_type