Graphical Inference in Linear-Gaussian State-Space Models

TL;DR

This paper introduces GraphEM, a novel method for estimating the transition matrix in linear-Gaussian state-space models by leveraging graph structures and advanced convex optimization, improving interpretability and efficiency.

Contribution

It presents a new graph-based perspective for transition matrix estimation in SSMs and develops a convex optimization approach with convergence guarantees.

Findings

GraphEM effectively estimates transition matrices in SSMs.

The method handles complex priors on graph structures.

Numerical examples demonstrate good performance and interpretability.

Abstract

State-space models (SSM) are central to describe time-varying complex systems in countless signal processing applications such as remote sensing, networks, biomedicine, and finance to name a few. Inference and prediction in SSMs are possible when the model parameters are known, which is rarely the case. The estimation of these parameters is crucial, not only for performing statistical analysis, but also for uncovering the underlying structure of complex phenomena. In this paper, we focus on the linear-Gaussian model, arguably the most celebrated SSM, and particularly in the challenging task of estimating the transition matrix that encodes the Markovian dependencies in the evolution of the multi-variate state. We introduce a novel perspective by relating this matrix to the adjacency matrix of a directed graph, also interpreted as the causal relationship among state dimensions in the Granger-causality sense. Under this perspective, we propose a new method called GraphEM based on the well sounded expectation-maximization (EM) methodology for inferring the transition matrix jointly with the smoothing/filtering of the observed data. We propose an advanced convex optimization solver relying on a consensus-based implementation of a proximal splitting strategy for solving the M-step. This approach enables an efficient and versatile processing of various sophisticated priors on the graph structure, such as parsimony constraints, while benefiting from convergence guarantees. We demonstrate the good performance and the interpretable results of GraphEM by means of two sets of numerical examples.