Work

Recursive Entropic Variational Inference for Nonlinear State-Space Models

Year: 2025

Type: preprint

Abstract: We present a class of algorithms for state estimation in nonlinear, non-Gaussian state-space models. Our approach is based on a variational Lagrangian formulation that casts Bayesian inference as a sequence of entropic trust-region updates subject to dynamic consistency constraints. This framework gives rise to a family of forward-backward algorithms whose structure is determined by the chosen factorization of the variational posterior. By focusing on Gauss--Markov approximations, we derive recursive schemes with favorable computational complexity. For general nonlinear, non-Gaussian models, we close the recursions using generalized statistical linear regression and Fourier--Hermite moment matching.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2511.15409

Authors: Abdulsamad, Hany, García-Fernández, Ángel F., Särkkä, Simo

Institutions: Amsterdam University of Applied Sciences, University of Amsterdam, Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Aalto University

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7106224503

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