Result: Fast Python sampler of the von Mises Fisher distribution

Title:
Fast Python sampler of the von Mises Fisher distribution
Échantillonneur rapide de la distribution de von Mises Fisher en Python
Authors:
Pinzón, Carlos
Contributors:
Concurrency, Mobility and Transactions (COMETE), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), European Project: 835294,H2020 Pilier ERC,HYPATIA(2019)
Publisher Information:
HAL CCSD, 2023.
Publication Year:
2023
Collection:
collection:X
collection:CNRS
collection:INRIA
collection:LIX
collection:LIX-COMETE
collection:INRIA-SACLAY
collection:INSMI
collection:X-LIX
collection:X-DEP
collection:X-DEP-INFO
collection:INRIA_TEST
collection:TESTALAIN1
collection:INRIA2
collection:IP_PARIS
collection:IP_PARIS_COPIE
collection:GS-COMPUTER-SCIENCE
Subject Terms:
Von Mises Fisher distribution, Python Implementation, Sampling Algorithms, [INFO]Computer Science [cs], [MATH]Mathematics [math]
Original Identifier:
HAL: hal-04004568
Document Type:
Electronic Resource preprint<br />Preprints<br />Working Papers
Language:
English
Relation:
info:eu-repo/grantAgreement//835294/EU/Privacy and Utility Allied/HYPATIA
Access URL:
https://hal.science/hal-04004568
https://hal.science/hal-04004568/document
https://hal.science/hal-04004568/file/main.pdf
Rights:
info:eu-repo/semantics/OpenAccess
URL: http://hal.archives-ouvertes.fr/licences/publicDomain/
Accession Number:
edshal.hal.04004568v1
Database:
HAL

Further Information

This paper implements a method for sampling from the d-dimensional Von Mises Fisher distribution using NumPy, focusing on speed and readability. The complexity of the algorithm is O(nd) for n samples, which is theoretically optimal taking into account that nd is the output size.