Saddlepoint approximations for the Bingham and Fisher–Bingham normalising constants

By A. Kume, Andrew T. A. Wood. 2005

119 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Geometry and Topology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8eb0f374da7a84febf4859cf8a13cdd6a812e3fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Eduardo Garc'ia-Portugu'es
2. Ian L. Dryden
3. Thomas Verdebout
4. Davy Paindaveine

In Geometry and Topology

1. Thomas Verdebout
2. Eduardo Garc'ia-Portugu'es
3. Ian L. Dryden

In Statistical and Nonlinear Physics

1. Ian L. Dryden
2. Davy Paindaveine

Citations in the corpus

• Statistical analysis on high-dimensional spheres and shape spaces (2005, methodology): “can be expressed in terms of the density of a linear combination of noncentral χ2 1 random variables [18], which can be evaluated using a saddlepoint approximation.”
• On Optimal Tests for Rotational Symmetry Against New Classes of Hyperspherical Distributions (2017, background): “…symmetric distributions; (ii) the densities of tangent elliptical distributions involve normalizing constants that are simple to compute (see, e.g., Kume and Wood (2005) for the delicate problem of approximating normalizing constants in the Fisher–Bingham model); (iii) simulation is…”