Published 2020 | Version v1
Journal article Metadata-only

BAT.jl Upgrading the Bayesian Analysis Toolkit

Creators

  • 1. Max Planck Institute for Physics, Munich (Germany)
  • 2. TU Dortmund University, Dortmund (Germany)

Description

In all but the simplest cases, performing data analysis based on Bayesian reasoning requires the use of advanced algorithms. The Bayesian Analysis Toolkit (BAT) provides a collection of algorithms and methods that facilitate the application of Bayesian statistics to user-defined problems of arbitrary complexity. With BAT.jl, we present a modern rewrite of BAT in the Julia programming language. Through the use of a modular software design that is capable of running parallel and distributed, and by extending the tool with new sampling and integration algorithms, BAT.jl is a high-performance framework for Bayesian inference, meeting the requirements of modern data analysis.

Availability note (English)

Available from https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06001.pdf; https://doaj.org/article/23f55fb0c65b4a158407c7c01e934984

Additional details

Identifiers

URL
https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06001.pdf; https://doaj.org/article/23f55fb0c65b4a158407c7c01e934984;
DOI
10.1051/epjconf/202024506001;

Publishing Information

Journal Title
EPJ. Web of Conferences
Journal Volume
245
Journal Page Range
vp.
ISSN
2100-014X

Conference

Title
24. International Conference on Computing in High Energy and Nuclear Physics
Acronym
CHEP 2019
Dates
4-8 Nov 2019
Place
Adelaide (Australia)

INIS

Country of Publication
France
Country of Input or Organization
France
INIS RN
53090227
Subject category
S97: MATHEMATICAL METHODS AND COMPUTING;
Resource subtype / Literary indicator
Conference
Descriptors DEI
ALGORITHMS; BAYESIAN STATISTICS; COMPUTER CODES; DATA ANALYSIS; DESIGN; PERFORMANCE; PROGRAMMING LANGUAGES; SAMPLING
Descriptors DEC
DATA PROCESSING; MATHEMATICAL LOGIC; MATHEMATICS; PROCESSING; STATISTICS