tf.contrib.distributions.MultivariateNormalDiag.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.MultivariateNormalDiag.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

class tf.contrib.distributions.MultivariateNormalDiag The multivariate normal distribution on R^k. This distribution is defined by a 1-D mean mu and a 1-D diagonal diag_stdev, representing the standard deviations. This distribution assumes the random variables, (X_1,...,X_k) are independent, thus no non-diagonal terms of the covariance matrix are needed. This allows for O(k) pdf evaluation, sampling, and storage.

tf.contrib.distributions.MultivariateNormalCholesky.__init__(mu, chol, validate_args=False, allow_nan_stats=True, name='MultivariateNormalCholesky') Multivariate Normal distributions on R^k. User must provide means mu and chol which holds the (batch) Cholesky factors, such that the covariance of each batch member is chol chol^T. Args: mu: (N+1)-D floating point tensor with shape [N1,...,Nb, k], b >= 0. chol: (N+2)-D Tensor with same dtype as mu and shape [N1,...,Nb, k, k]. The upper trian

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

tf.contrib.distributions.MultivariateNormalCholesky.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.distributions.MultivariateNormalCholesky.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.contrib.distributions.MultivariateNormalCholesky.std(name='std') Standard deviation.

tf.contrib.distributions.MultivariateNormalCholesky.sigma_det(name='sigma_det') Determinant of covariance matrix.

tf.contrib.distributions.MultivariateNormalCholesky.sigma Dense (batch) covariance matrix, if available.