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Originalveröffentlichung
DOI: 10.1109/SDF.2015.7347705

Stochastic Sampling of the Hyperspherical von Mises-Fisher Distribution Without Rejection Methods

Kurz, Gerhard; Hanebeck, Uwe D.

Abstract:
We propose a novel sampling algorithm for the
von Mises–Fisher distribution on the unit hypersphere. Unlike
previous works, we show a solution for an arbitrary number of
dimensions without requiring rejection sampling. As a result, the
proposed algorithm has a deterministic runtime. The key idea
consists in applying the inversion method to a one-dimensional
subproblem and analytically calculating the integral occurring in
the distribution function. The proposed method is most efficient
for odd numbers of dimensions. We compare the algorithm to a
state-of-the-art rejection sampling method in simulations.


Zugehörige Institution(en) am KIT Deutsch-Französisches Institut für Umweltforschung - Teilinstitut Karlsruhe (DFIU - Teilinstitut Karlsruhe)
Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Jahr 2015
Sprache Englisch
Identifikator ISBN: 978-1-4673-7175-9
KITopen ID: 1000051079
Erschienen in Proceedings of the 10th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015), 06 - 08 October 2015, Bonn, Germany
Verlag IEEE, Piscataway (NJ)
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