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

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

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

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

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

tf.contrib.distributions.WishartCholesky.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.WishartCholesky.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

class tf.contrib.distributions.WishartCholesky The matrix Wishart distribution on positive definite matrices. This distribution is defined by a scalar degrees of freedom df and a lower, triangular Cholesky factor which characterizes the scale matrix. Using WishartCholesky is a constant-time improvement over WishartFull. It saves an O(nbk^3) operation, i.e., a matrix-product operation for sampling and a Cholesky factorization in log_prob. For most use-cases it often saves another O(nbk^3) opera

tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, validate_args=False, allow_nan_stats=True, name='Uniform') Construct Uniform distributions with a and b. The parameters a and b must be shaped in a way that supports broadcasting (e.g. b - a is a valid operation). Here are examples without broadcasting: # Without broadcasting u1 = Uniform(3.0, 4.0) # a single uniform distribution [3, 4] u2 = Uniform([1.0, 2.0], [3.0, 4.0]) # 2 distributions [1, 3], [2, 4] u3 = Uniform([[1.0, 2.0],