DegenDetector: Symbolic Recovery of Parameter Degeneracies in Bayesian Posteriors

Abstract

We introduce DegenDetector, a framework for identifying and characterizing parameter degeneracies in posterior distributions as closed-form symbolic equations. By combining mutual information screening with alternating symbolic regression, we facilitate automated and interpretable identification of degenerate relationships without domain-specific input. While standard tools such as corner plots can indicate that correlations exist, they do not reveal the underlying functional form. DegenDetector fills this gap by expressing multi-parameter degeneracies as closed-form equations, providing interpretable structure that scales to high-order parameter spaces.