tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.__init__()

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 = SS^T = SS = (M + V D V^T) (M + V D V^T)
M is diagonal (k x k)
V = is shape (k x r), typically r << k
D = is diagonal (r x r), optional (defaults to identity).

Args:

• mu: Rank n + 1 floating point tensor with shape [N1,...,Nn, k], n >= 0. The means.
• diag_large: Optional rank n + 1 floating point tensor, shape [N1,...,Nn, k] n >= 0. Defines the diagonal matrix M.
• v: Rank n + 1 floating point tensor, shape [N1,...,Nn, k, r] n >= 0. Defines the matrix V.
• diag_small: Rank n + 1 floating point tensor, shape [N1,...,Nn, k] n >= 0. Defines the diagonal matrix D. Default is None, which means D will be the identity matrix.
• validate_args: Boolean, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
• allow_nan_stats: Boolean, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
• name: The name to give Ops created by the initializer.