tf.contrib.distributions.quadrature_scheme_softmaxnormal_gauss_hermite

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

Defined in tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py.

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

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.