tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample') Generate samples of the

tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf') Log probability density function.

tf.contrib.distributions.Exponential.survival_function(value, name='survival_function') Survival function. Given

tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function') Survival function.

tf.contrib.distributions.Beta.mode(name='mode') Mode. Additional documentation from Beta:

tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob') Log probability density/mass function (depending

class tf.contrib.distributions.NormalWithSoftplusSigma Normal with softplus applied to sigma.

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

class tf.contrib.distributions.Poisson Poisson distribution. The Poisson distribution is parameterized

tf.contrib.distributions.BernoulliWithSigmoidP.is_continuous