class tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor Base Class for Tensor-like objects that emit stochastic values.

tf.contrib.distributions.Laplace.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.Chi2.mean(name='mean') Mean.

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

tf.contrib.distributions.Dirichlet.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is

tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.Categorical.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.value_type

tf.contrib.bayesflow.variational_inference.register_prior(variational, prior) Associate a variational DistributionTensor with a Distribution prior. This is a helper function used in conjunction with elbo that allows users to specify the mapping between variational distributions and their priors without having to pass in variational_with_prior explicitly. Args: variational: DistributionTensor q(Z). Approximating distribution. prior: Distribution p(Z). Prior distribution. Returns: None Raise