Consistent Application of Maximum Entropy to Quantum-Monte-Carlo Data

TL;DR

This paper introduces a Bayesian maximum entropy method that consistently incorporates error covariance in Quantum-Monte-Carlo data analysis, improving the inference of dynamic properties in strongly correlated fermion systems.

Contribution

It presents a novel closed Bayesian framework that properly accounts for error covariance in QMC data, addressing a gap in current methods.

Findings

Enhanced accuracy in dynamic property inference

Consistent treatment of QMC data errors

Improved reliability of Bayesian maximum entropy applications

Abstract

Bayesian statistics in the frame of the maximum entropy concept has widely been used for inferential problems, particularly, to infer dynamic properties of strongly correlated fermion systems from Quantum-Monte-Carlo (QMC) imaginary time data. In current applications, however, a consistent treatment of the error-covariance of the QMC data is missing. Here we present a closed Bayesian approach to account consistently for the QMC-data.