Software packages for holonomic gradient method

Tamio Koyama, Hiromasa Nakayama, Katsuyoshi Ohara, Tomonari Sei, Nobuki Takayama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We present software packages for the holonomic gradient method (HGM). These packages compute normalizing constants and the probabilities of some regions. While many algorithms which compute integrals over high-dimensional regions utilize the Monte-Carlo method, our HGM utilizes algorithms for solving ordinary differential equations such as the Runge-Kutta-Fehlberg method. As a result, our HGM can evaluate many integrals with a high degree of accuracy and moderate computational time. The source code of our packages is distributed on our web page [12].

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages706-712
Number of pages7
Volume8592 LNCS
ISBN (Print)9783662441985
DOIs
Publication statusPublished - 2014
Event4th International Congress on Mathematical Software, ICMS 2014 - Seoul, Korea, Republic of
Duration: 2014 Aug 52014 Aug 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8592 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Congress on Mathematical Software, ICMS 2014
CountryKorea, Republic of
CitySeoul
Period14/8/514/8/9

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Keywords

  • Bingham prior
  • holonomic gradient method
  • normalizing constant
  • R project
  • region probability

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Koyama, T., Nakayama, H., Ohara, K., Sei, T., & Takayama, N. (2014). Software packages for holonomic gradient method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8592 LNCS, pp. 706-712). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8592 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-662-44199-2_105