Bayesian estimation of correlation functions - Ángel Gutiérrez-Rubio; Juan S. Rojas-Arias; Jun Yoneda; Seigo Tarucha; Daniel Loss; Peter Stano (Physical Review Research, 2022)
We apply Bayesian statistics to the estimation of correlation functions. We give the probability distributions of auto- and cross-correlations as functions of the data. Our procedure uses the measured data optimally and informs about the certainty level of the estimation. Our results apply to general stationary processes and their essence is a nonparametric estimation of spectra. It allows one to better understand the statistical noise fluctuations, assess the correlations between two variables, and postulate parametric models of spectra that can be further tested. We also propose a method to numerically generate correlated noise with a given spectrum.