# GNA: new framework for statistical data analysis

## TL;DR

GNA is a flexible, efficient framework for large-scale physical model fitting using data flow graphs, enabling uncertainty propagation and statistical analysis.

## Contribution

It introduces a novel data flow-based framework for fitting and analyzing large-scale physical models with uncertainty handling.

## Key findings

- Supports large parameter sets and complex models
- Enables uncertainty propagation with correlations
- Provides efficient, lazy evaluation of models

## 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.