# Inverse Problems in PDF Determinations

## TL;DR

This paper introduces a Bayesian framework to address the challenging inverse problem of determining Parton Distribution Functions from limited data, emphasizing uncertainty quantification and initial testing results.

## Contribution

It presents a novel Bayesian approach for PDF determination and provides initial results demonstrating its effectiveness in solving inverse problems.

## Key findings

- Bayesian method successfully applied to PDF determination
- Initial closure test results show promising accuracy
- Framework enhances uncertainty quantification in inverse problems

## Abstract

The determination of Parton Distribution Functions from a finite set of data is a typical example of an inverse problem. Inverse problems are notoriously difficult to solve, in particular when a robust determination of the uncertainty in the result is needed. We present a Bayesian framework to deal with this problem and discuss first results from a closure test.