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.Dirichlet.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.distributions.StudentT.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

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

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

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.