tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob()

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

2016-10-14 13:02:32
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.mean()

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.mean(name='mean') Mean. Additional

2016-10-14 12:55:07
tf.contrib.distributions.Chi2.sample_n()

tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n') Generate n samples.

2016-10-14 12:49:31
tf.contrib.distributions.Binomial.cdf()

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

2016-10-14 12:47:14
tf.contrib.distributions.BetaWithSoftplusAB.survival_function()

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

2016-10-14 12:47:06
tf.contrib.distributions.QuantizedDistribution.log_pdf()

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

2016-10-14 13:01:25
tf.contrib.distributions.Gamma.get_batch_shape()

tf.contrib.distributions.Gamma.get_batch_shape() Shape of a single sample from a single event index as a TensorShape

2016-10-14 12:53:12
tf.contrib.distributions.Beta.

tf.contrib.distributions.Beta.__init__(a, b, validate_args=False, allow_nan_stats=True, name='Beta') Initialize a batch of Beta

2016-10-14 12:46:47
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob()

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

2016-10-14 12:53:59
tf.contrib.distributions.DirichletMultinomial.validate_args

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

2016-10-14 12:50:55