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

tf.contrib.distributions.Multinomial.p Event probabilities.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGammaWithSoftplusAlphaBeta')

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

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

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

tf.contrib.distributions.QuantizedDistribution.is_continuous

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

tf.contrib.distributions.Chi2.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor

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