| Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
|---|---|---|
| Article Number | 05024 | |
| Number of page(s) | 9 | |
| Section | T5 - Software development | |
| DOI | https://doi.org/10.1051/epjconf/201921405024 | |
| Published online | 17 September 2019 | |
https://doi.org/10.1051/epjconf/201921405024
GNA: new framework for statistical data analysis
Joint Institute for Nuclear Research, Dubna,
Moscow,
Russia
* e-mail: gonchar@jinr.ru
Published online: 17 September 2019
We report on the status of GNA — a new framework for fitting large-scale 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.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.