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
tf.contrib.distributions.InverseGamma.get_event_shape()

tf.contrib.distributions.InverseGamma.get_event_shape() Shape of a single sample from a single batch as a TensorShape

2016-10-14 12:54:24
tf.contrib.distributions.Distribution.log_cdf()

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

2016-10-14 12:51:14
tf.contrib.distributions.Binomial.variance()

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

2016-10-14 12:48:00
tf.contrib.distributions.StudentT.log_pmf()

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

2016-10-14 13:02:03
tf.contrib.distributions.MultivariateNormalFull.batch_shape()

tf.contrib.distributions.MultivariateNormalFull.batch_shape(name='batch_shape') Shape of a single sample from a single event index

2016-10-14 12:59:01