tf.contrib.distributions.quadrature_scheme_softmaxnormal_gauss_hermite

Use Gauss-Hermite quadrature to form quadrature on K - 1 simplex. (deprecated)

tf.contrib.distributions.quadrature_scheme_softmaxnormal_gauss_hermite(
    normal_loc, normal_scale, quadrature_size, validate_args=False, name=None
)

A SoftmaxNormal random variable Y may be generated via

Y = SoftmaxCentered(X),
X = Normal(normal_loc, normal_scale)

Note: for a given quadrature_size, this method is generally less accurate than quadrature_scheme_softmaxnormal_quantiles.

Args
normal_loc	float-like Tensor with shape [b1, ..., bB, K-1], B>=0. The location parameter of the Normal used to construct the SoftmaxNormal.
normal_scale	float-like Tensor. Broadcastable with normal_loc. The scale parameter of the Normal used to construct the SoftmaxNormal.
quadrature_size	Python int scalar representing the number of quadrature points.
validate_args	Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
name	Python str name prefixed to Ops created by this class.

Returns
grid	Shape [b1, ..., bB, K, quadrature_size] Tensor representing the convex combination of affine parameters for K components. grid[..., :, n] is the n-th grid point, living in the K - 1 simplex.
probs	Shape [b1, ..., bB, K, quadrature_size] Tensor representing the associated with each grid point.