tf.contrib.distributions.DirichletMultinomial.mode(name='mode') Mode.

tf.contrib.distributions.Normal.mu Distribution parameter for the mean.

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.graph

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.clone(name=None, **dist_args)

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

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.mean(name='mean') Mean. Additional documentation from InverseGamma: The mean of an inverse gamma distribution is beta / (alpha - 1), when alpha > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN

tf.contrib.distributions.LaplaceWithSoftplusScale.mode(name='mode') Mode.

class tf.FIFOQueue A queue implementation that dequeues elements in first-in first-out order. See tf.QueueBase for a description of the methods on this class.

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

tf.contrib.distributions.Uniform.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 u