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

We construct a 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 \cite{Lahav00} and \cite{Hobson02} which was designed to combine independent data sets. The hyperparameter matrix method we present here includes the relevant weights of multiple data sets and mutual correlations, and when the hyperparameters are marginalized over, the parameters of interest are recovered. 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 a classic model of fitting a straight line to two sets of data. We show that the hyperparameter matrix method can detect unaccounted systematics 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.