GNA: new framework for statistical data analysis

被引：0

作者：

Fatkina, Anna ^{[1
]}

Gonchar, Maxim ^{[1
]}

Kalitkina, Anastasia ^{[1
]}

Kolupaeva, Liudmila ^{[1
]}

Naumov, Dmitry ^{[1
]}

Selivanov, Dmitry ^{[1
]}

Treskov, Konstantin ^{[1
]}

机构：

[1] Joint Inst Nucl Res, Moscow, Russia

来源：

23RD INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS (CHEP 2018) | 2019年 / 214卷

基金：

俄罗斯基础研究基金会;

关键词：

D O I：

10.1051/epjconf/201921405024

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We report on the status of GNA - a new framework for fitting largescale physical models. GNA utilizes the data flow concept within which a model is represented by a directed acyclic graph. Each node is an operation on an array (matrix multiplication, derivative or cross section calculation, etc). The framework enables the user to create flexible and efficient large-scale lazily evaluated models, handle large numbers of parameters, propagate parameters' uncertainties while taking into account possible correlations between them, fit models, and perform statistical analysis. The main goal of the paper is to give an overview of the main concepts and methods as well as reasons behind their design. Detailed technical information is to be published in further works.

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页数：9

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