tf.contrib.distributions.MultivariateNormalFull.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.distribution

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

tf.contrib.distributions.Categorical.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.distributions.WishartCholesky.scale_operator_pd Wishart distribution scale matrix as an OperatorPD.

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.graph

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

tf.contrib.distributions.Chi2.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.

tf.VarLenFeature.__getnewargs__() Return self as a plain tuple. Used by copy and pickle.

class tf.contrib.distributions.MultivariateNormalDiag The multivariate normal distribution on R^k. This distribution is defined by a 1-D mean mu and a 1-D diagonal diag_stdev, representing the standard deviations. This distribution assumes the random variables, (X_1,...,X_k) are independent, thus no non-diagonal terms of the covariance matrix are needed. This allows for O(k) pdf evaluation, sampling, and storage.