tf.contrib.distributions.StudentT.is_reparameterized

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

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

tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

int64 tensorflow::Tensor::dim_size(int d) const Convenience accessor for the tensor shape.

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.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.graph

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