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

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

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.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.MultivariateNormalDiagPlusVDVT.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.MultivariateNormalDiagPlusVDVT.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 Stud

class tf.contrib.distributions.MultivariateNormalDiagPlusVDVT The multivariate normal distribution on R^k. Every batch member of this distribution is defined by a mean and a lightweight covariance matrix C.

tf.contrib.distributions.MultivariateNormalDiag.__init__(mu, diag_stdev, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiag') Multivariate Normal distributions on R^k. User must provide means mu and standard deviations diag_stdev. Each batch member represents a random vector (X_1,...,X_k) of independent random normals. The mean of X_i is mu[i], and the standard deviation is diag_stdev[i]. Args: mu: Rank N + 1 floating point tensor with shape [N1,...,Nb, k], b >= 0. di

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

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