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arXiv · 1903.05567

GNA: new framework for statistical data analysis

Anna Fatkina·Maxim Gonchar·Anastasia Kalitkina·Liudmila Kolupaeva·Dmitry Naumov·Dmitry Selivanov·Konstantin Treskov
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Abstract

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.

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Anna Fatkina, Maxim Gonchar, Anastasia Kalitkina, Liudmila Kolupaeva, Dmitry Naumov, Dmitry Selivanov, Konstantin Treskov. 2019-03-13. GNA: new framework for statistical data analysis. https://doi.org/10.1051/epjconf%2F201921405024

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