How to combine correlated data sets -- A Bayesian hyperparameter matrix method

TL;DR

This paper introduces a Bayesian hyperparameter matrix method for joint analysis of correlated astronomical data sets, effectively handling mutual correlations and detecting systematic errors.

Contribution

It generalizes previous hyperparameter methods to account for correlations between data sets, providing a simplified likelihood formula and demonstrating its effectiveness.

Findings

Method detects unaccounted systematic errors.

Likelihood simplifies joint analysis of correlated data.

Bayes' factors indicate the need for hyperparameters.

Abstract

We construct a "hyperparameter matrix" statistical method for performing the joint analyses of multiple correlated astronomical data sets, in which the weights of data sets are determined by their own statistical properties. This method is a generalization of the hyperparameter method constructed by Lahav et al. (2000) and Hobson, Bridle, & Lahav (2002) which was designed to combine independent data sets. The advantage of our method is to treat correlations between multiple data sets and gives appropriate relevant weights of multiple data sets with mutual correlations. We define a new "element-wise" product, which greatly simplifies the likelihood function with hyperparameter matrix. We rigorously prove the simplified formula of the joint likelihood and show that it recovers the original hyperparameter method in the limit of no covariance between data sets. We then illustrate the method by applying it to a demonstrative toy model of fitting a straight line to two sets of data. We show that the hyperparameter matrix method can detect unaccounted systematic errors or underestimated errors in the data sets. Additionally, the ratio of Bayes' factors provides a distinct indicator of the necessity of including hyperparameters. Our example shows that the likelihood we construct for joint analyses of correlated data sets can be widely applied to many astrophysical systems.