Proof of The TAP Free Energy for High-Dimensional Linear Regression with Spherical Priors at All Temperatures

Variational inference (VI) has emerged as an efficient framework for approximating intractable posteriors. Traditional mean-field VI methods fail to capture the complex dependencies and can be inconsistent in high-dimensional settings. In contrast, it has been conjectured, based on statistical physics heuristics and conditions for the stationary points, that the more refined Thouless-Anderson-Palmer (TAP) approximation is consistent in the high-dimensional regimes. Existing results, however, have only proved such TAP representations in high-temperature regimes, even in the basic setting of spherical priors (Qiu and Sen, Ann. Appl. Probab. 2023). In this work, we prove the asymptotic correctness of TAP free energy approximation for Bayesian linear regression under uniform spherical priors at any temperature ($Δ>0$). The key idea in our proof is to find a ridge regression functional that dominates the TAP free energy.