tf.contrib.distributions.WishartFull.variance()

tf.contrib.distributions.WishartFull.variance(name='variance') Variance.

2016-10-14 13:04:42
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob()

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

2016-10-14 12:55:11
tf.contrib.distributions.WishartCholesky.allow_nan_stats

tf.contrib.distributions.WishartCholesky.allow_nan_stats Python boolean describing behavior when a stat is undefined.

2016-10-14 13:03:49
tf.contrib.distributions.WishartFull.allow_nan_stats

tf.contrib.distributions.WishartFull.allow_nan_stats Python boolean describing behavior when a stat is undefined.

2016-10-14 13:04:17
tf.contrib.distributions.Mixture.param_shapes()

tf.contrib.distributions.Mixture.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the

2016-10-14 12:56:33
tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf()

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

2016-10-14 13:00:26
tf.contrib.distributions.Multinomial.param_static_shapes()

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

2016-10-14 12:57:04
tf.contrib.distributions.Gamma.pmf()

tf.contrib.distributions.Gamma.pmf(value, name='pmf') Probability mass function. Args:

2016-10-14 12:53:37
tf.contrib.distributions.InverseGamma.param_shapes()

tf.contrib.distributions.InverseGamma.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given

2016-10-14 12:54:40
tf.contrib.distributions.BernoulliWithSigmoidP.validate_args

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

2016-10-14 12:45:56