tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function') Log survival function.

tf.contrib.distributions.Normal.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor

tf.contrib.distributions.BernoulliWithSigmoidP.__init__(p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='BernoulliWithSigmoidP')

tf.contrib.distributions.NormalWithSoftplusSigma.sample_n(n, seed=None, name='sample_n') Generate n samples.

tf.contrib.distributions.Distribution.__init__(dtype, parameters, is_continuous, is_reparameterized, validate_args, allow_nan_stats, name=None)

tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf') Cumulative distribution function. Given

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

tf.contrib.distributions.LaplaceWithSoftplusScale.event_shape(name='event_shape') Shape of a single sample from a single batch

class tf.contrib.distributions.Laplace The Laplace distribution with location and scale > 0 parameters.

tf.contrib.distributions.Exponential.__init__(lam, validate_args=False, allow_nan_stats=True, name='Exponential') Construct Exponential