Bat.Jl - Upgrading The Bayesian Analysis Toolkit

24TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS (CHEP 2019)(2020)

引用 1|浏览7
暂无评分
摘要
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.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要