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.

tf.IdentityReader.reset(name=None) Restore a reader to its initial clean state. Args: name: A name for the operation (optional). Returns: The created Operation.

tf.contrib.distributions.WishartFull.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Gamma.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.TransformedDistribution.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from TransformedDistribution: Implements (log o p o g)(y) - (log o det o J o g)(y), where g is the inverse of transform. Also raises a ValueError if inverse was not provided to the distribution and y was not returned from sample. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob:

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.graph

tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.square(x, name=None) Computes square of x element-wise. I.e., (y = x * x = x^2). Args: x: A Tensor or SparseTensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor or SparseTensor. Has the same type as x.

tf.matrix_solve(matrix, rhs, adjoint=None, name=None) Solves systems of linear equations. Matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. Rhs is a tensor of shape [..., M, K]. The output is a tensor shape [..., M, K]. If adjoint is False then each output matrix satisfies matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]. If adjoint is True then each output matrix satisfies adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]. Args: m

tf.contrib.framework.get_variables(scope=None, suffix=None, collection='variables') Gets the list of variables, filtered by scope and/or suffix. Args: scope: an optional scope for filtering the variables to return. suffix: an optional suffix for filtering the variables to return. collection: in which collection search for. Defaults to GraphKeys.VARIABLES. Returns: a list of variables in collection with scope and suffix.