# Overlap matrix concentration in optimal Bayesian inference

## TL;DR

This paper proves that in optimal Bayesian inference models with vector signals, the overlap matrix concentrates under perturbation, ensuring the validity of replica symmetric assumptions for various high-dimensional inference problems.

## Contribution

It demonstrates the concentration of the overlap matrix in Bayesian models with finite-dimensional vector components, validating replica symmetry in the optimal setting.

## Key findings

- Overlap matrix concentrates under perturbation.
- Results apply to low-rank tensor factorization.
- Validates replica symmetric assumptions in Bayesian inference.

## Abstract

We consider models of Bayesian inference of signals with vectorial components of finite dimensionality. We show that, under a proper perturbation, these models are replica symmetric in the sense that the overlap matrix concentrates. The overlap matrix is the order parameter in these models and is directly related to error metrics such as minimum mean-square errors. Our proof is valid in the optimal Bayesian inference setting. This means that it relies on the assumption that the model and all its hyper-parameters are known so that the posterior distribution can be written exactly. Examples of important problems in high-dimensional inference and learning to which our results apply are low-rank tensor factorization, the committee machine neural network with a finite number of hidden neurons in the teacher-student scenario, or multi-layer versions of the generalized linear model.