tf.contrib.distributions.StudentT.is_continuous

tf.contrib.distributions.StudentT.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.StudentT.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.distributions.StudentT.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.distributions.StudentT.df Degrees of freedom in these Student's t distribution(s).

tf.contrib.distributions.StudentT.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.StudentT.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.StudentT.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.StudentT.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is

tf.contrib.distributions.RegisterKL.__init__(dist_cls_a, dist_cls_b) Initialize the KL registrar. Args: dist_cls_a: the class of the first argument of the KL divergence. dist_cls_b: the class of the second argument of the KL divergence.