Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference

TL;DR

This paper introduces a balanced truncation method for reducing the computational complexity of Bayesian inference in large linear dynamical systems, by leveraging system-theoretic Gramians linked to the prior and Fisher information.

Contribution

It develops a novel Gramian-based balanced truncation approach tailored for Bayesian inference, enabling efficient approximation of posterior distributions in high-dimensional systems.

Findings

Achieves near-optimal posterior covariance approximation

Provides significant state dimension reduction

Ensures stability and error bounds in reduced models

Abstract

We consider the Bayesian approach to the linear Gaussian inference problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken after the initial time. In practical applications, the large dimension of the dynamical system state poses a computational obstacle to computing the exact posterior distribution. Model reduction offers a variety of computational tools that seek to reduce this computational burden. In particular, balanced truncation is a system-theoretic approach to model reduction which obtains an efficient reduced-dimension dynamical system by projecting the system operators onto state directions which trade off the reachability and observability of state directions as expressed through the associated Gramians. We introduce Gramian definitions relevant to the inference setting and propose a balanced truncation approach based on these inference Gramians that yield a reduced dynamical system that can be used to cheaply approximate the posterior mean and covariance. Our definitions exploit natural connections between (i) the reachability Gramian and the prior covariance and (ii) the observability Gramian and the Fisher information. The resulting reduced model then inherits stability properties and error bounds from system theoretic considerations, and in some settings yields an optimal posterior covariance approximation. Numerical demonstrations on two benchmark problems in model reduction show that our method can yield near-optimal posterior covariance approximations with order-of-magnitude state dimension reduction.