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

return tensorflow::Status::OK()

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

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value_type

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

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

class tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor TransformedDistributionTensor is a StochasticTensor backed by the distribution TransformedDistribution.

class tf.contrib.distributions.DirichletMultinomial DirichletMultinomial mixture distribution. This distribution is parameterized by a vector alpha of concentration parameters for k classes and n, the counts per each class..

tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf') Log probability density function. 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. Raises: TypeError: if not is_continuous.