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

tf.contrib.distributions.Chi2.df

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

tf.contrib.distributions.MultivariateNormalFull.variance(name='variance') Variance.

tf.contrib.distributions.MultivariateNormalCholesky.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.Beta.__init__(a, b, validate_args=False, allow_nan_stats=True, name='Beta') Initialize a batch of Beta distributions. Args: a: Positive floating point tensor with shape broadcastable to [N1,..., Nm] m >= 0. Defines this as a batch of N1 x ... x Nm different Beta distributions. This also defines the dtype of the distribution. b: Positive floating point tensor with shape broadcastable to [N1,..., Nm] m >= 0. Defines this as a batch of N1 x ... x Nm different Beta

tf.contrib.distributions.StudentT.is_continuous

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

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