Proximal Approximate Inference in State-Space Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918209562607616 |
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| author | Abdulsamad, Hany García-Fernández, Ángel F. Särkkä, Simo |
| author_facet | Abdulsamad, Hany García-Fernández, Ángel F. Särkkä, Simo |
| contents | 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 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_15409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Proximal Approximate Inference in State-Space Models Abdulsamad, Hany García-Fernández, Ángel F. Särkkä, Simo Machine Learning Methodology 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 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. |
| title | Proximal Approximate Inference in State-Space Models |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2511.15409 |