Matching Bayesian and frequentist coverage probabilities when using an approximate data covariance matrix

Observational astrophysics consists of making inferences about the Universe by comparing data and models. The credible intervals placed on model parameters are often as important as the maximum a posteriori probability values, as the intervals indicate concordance or discordance between models and with measurements from other data. Intermediate statistics (e.g. the power spectrum) are usually measured and inferences are made by fitting models to these rather than the raw data, assuming that the likelihood for these statistics has multivariate Gaussian form. The covariance matrix used to calculate the likelihood is often estimated from simulations, such that it is itself a random variable. This is a standard problem in Bayesian statistics, which requires a prior to be placed on the true model parameters and covariance matrix, influencing the joint posterior distribution. As an alternative to the commonly used independence Jeffreys prior, we introduce a prior that leads to a posterior that has approximately frequentist matching coverage. This is achieved by matching the covariance of the posterior to that of the distribution of true values of the parameters around the maximum likelihood values in repeated trials, under certain assumptions. Using this prior, credible intervals derived from a Bayesian analysis can be interpreted approximately as confidence intervals, containing the truth a certain proportion of the time for repeated trials. Linking frequentist and Bayesian approaches that have previously appeared in the astronomical literature, this offers a consistent and conservative approach for credible intervals quoted on model parameters for problems where the covariance matrix is itself an estimate.

Automated summary

Observational astrophysics involves making inferences about the Universe through data and model comparisons. Credible intervals for model parameters are as important as maximum a posteriori probability values, and intermediate statistics are used to fit models to data. The likelihood for these statistics is usually assumed to have a multivariate Gaussian form and the covariance matrix is estimated from simulations. We introduce a prior that matches the covariance of the posterior to the distribution of true values around the maximum likelihood values, offering a consistent and conservative approach for credible intervals.