Recursive Entropic Variational Inference for Nonlinear State-Space Models
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Abstract (arXiv)
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.
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References · 25
- 2016Variational Inference: A Review for Statisticianscited by 1,472
- 2013Black Box Variational Inferencecited by 319
- 2013Expectation Propagation for approximate Bayesian inferencecited by 174
- 2016R'enyi Divergence Variational Inferencecited by 104
- 2017Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Modelscited by 65
- 2018Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspectivecited by 31
- 2025SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equationscited by 23
- 2020Fast Variational Learning in State-Space Gaussian Process Modelscited by 20
- 2021Online Variational Filtering and Parameter Learningcited by 19
- 2021Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equationscited by 13
- 2021Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guaranteescited by 13
- 2023Variational Gaussian Process Diffusion Processescited by 11
13 more
- 2020State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processescited by 10
- 2015A trust-region method for stochastic variational inference with applications to streaming datacited by 9
- 2019Exactly Sparse Gaussian Variational Inference with Application to Derivative-Free Batch Nonlinear State Estimationcited by 7
- 2021Moment-Based Variational Inference for Stochastic Differential Equationscited by 6
- 2022Parallel square-root statistical linear regression for inference in nonlinear state space modelscited by 6
- 2025SING: SDE Inference via Natural Gradientscited by 5
- 2020Gaussian Variational State Estimation for Nonlinear State-Space Modelscited by 3
- 2023Parallel-in-Time Probabilistic Numerical ODE Solverscited by 3
- 2022Fourier--Hermite Dynamic Programming for Optimal Controlcited by 2
- 2017Damped Posterior Linearization Filtercited by 1
- 2021Posterior linearisation smoothing with robust iterationscited by 1
- 2023Variational Gaussian filtering via Wasserstein gradient flowscited by 1
- 2025A Recursive Theory of Variational State Estimation: The Dynamic Programming Approachcited by 1
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- 2022Linear Variational State-Space Filtering
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- 2023Variational excess risk bound for general state space models