tf.contrib.distributions.ExponentialWithSoftplusLam.pmf()

tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf') Probability mass function.

2016-10-14 12:52:53
tf.contrib.distributions.Exponential.prob()

tf.contrib.distributions.Exponential.prob(value, name='prob') Probability density/mass function (depending on is_continuous)

2016-10-14 12:52:27
tf.contrib.distributions.MultivariateNormalCholesky.sample_n()

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

2016-10-14 12:57:45
tf.contrib.distributions.Chi2.mode()

tf.contrib.distributions.Chi2.mode(name='mode') Mode. Additional documentation from Gamma:

2016-10-14 12:49:16
tf.contrib.distributions.MultivariateNormalCholesky.cdf()

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

2016-10-14 12:57:18
tf.contrib.distributions.Uniform.is_reparameterized

tf.contrib.distributions.Uniform.is_reparameterized

2016-10-14 13:03:19
tf.contrib.distributions.DirichletMultinomial.

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

2016-10-14 12:50:57
tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.is_continuous

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.is_continuous

2016-10-14 13:02:31
tf.contrib.distributions.Uniform.param_static_shapes()

tf.contrib.distributions.Uniform.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes.

2016-10-14 13:03:33
tf.contrib.distributions.TransformedDistribution.log_det_jacobian

tf.contrib.distributions.TransformedDistribution.log_det_jacobian Function computing the log determinant of the Jacobian of transform

2016-10-14 13:02:48