class tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev MultivariateNormalDiag with diag_stddev = softplus(diag_stddev).

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.__init__(mu, diag_large, v, diag_small=None, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagPlusVDVT') Multivariate Normal distributions on R^k. For every batch member, this distribution represents k random variables (X_1,...,X_k), with mean E[X_i] = mu[i], and covariance matrix C_{ij} := E[(X_i - mu[i])(X_j - mu[j])] The user initializes this class by providing the mean mu, and a lightweight definition of C: C = S

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

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

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

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

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

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.