tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.param_static_shapes()

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.param_static_shapes(cls, sample_shape) param_shapes with static (i.e.

2016-10-14 12:58:41
tf.contrib.distributions.InverseGamma.log_pmf()

tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf') Log probability mass function. Args:

2016-10-14 12:54:31
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample()

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

2016-10-14 12:58:55
tf.contrib.distributions.NormalWithSoftplusSigma.prob()

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

2016-10-14 13:00:29
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.mean()

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.mean(name='mean') Mean.

2016-10-14 12:54:00
tf.contrib.distributions.Gamma.survival_function()

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

2016-10-14 12:53:47
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf()

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf') Log cumulative distribution function.

2016-10-14 12:58:36
tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.mode()

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

2016-10-14 13:02:33
tf.contrib.distributions.StudentT.

tf.contrib.distributions.StudentT.__init__(df, mu, sigma, validate_args=False, allow_nan_stats=True, name='StudentT') Construct

2016-10-14 13:02:28
tf.contrib.distributions.Beta.std()

tf.contrib.distributions.Beta.std(name='std') Standard deviation.

2016-10-14 12:46:42