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

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

tf.contrib.distributions.WishartFull.dimension Dimension of underlying vector space. The p in R^(p*p).

tf.contrib.distributions.WishartFull.df Wishart distribution degree(s) of freedom.

tf.contrib.distributions.WishartFull.cholesky_input_output_matrices Boolean indicating if Tensor input/outputs are Cholesky factorized.

tf.contrib.distributions.WishartFull.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.WishartFull.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.WishartFull.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

class tf.contrib.distributions.WishartFull The matrix Wishart distribution on positive definite matrices. This distribution is defined by a scalar degrees of freedom df and a symmetric, positive definite scale matrix. Evaluation of the pdf, determinant, and sampling are all O(k^3) operations where (k, k) is the event space shape.