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Cox, B.,
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Segarra, S.,
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(2024). End-to-end learning of Gaussian mixture proposals using differentiable particle filters and neural networks. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 9701-9705). IEEE.
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Sevilla, M.,
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Cox, B.,
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Elvira, V.,
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(2023). State and Dynamics Estimation with the Kalman–Langevin filter. In 2023 57th Asilomar Conference on Signals, Systems, and Computers (pp. 1372-1376). IEEE.
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Pérez-Vieites, S.,
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(2023). Adaptive Gaussian nested filter for parameter estimation and state tracking in dynamical systems. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
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(2020). A nested hybrid filter for parameter estimation and state tracking in homogeneous multi-scale models. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE.
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(2019). Second Order Subspace Statistics for Adaptive State-Space Partitioning in Multiple Particle Filtering. In 2019 IEEE 8th Intenational Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (pp. 609-613). IEEE.
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Iqbal, S.,
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(2024). Recursive nested filtering for efficient amortized Bayesian experimental design. arXiv preprint arXiv:2409.05354.
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(2022). Nested smoothing algorithms for inference and tracking of heterogeneous multi-scale state-space systems. arXiv preprint arXiv:2204.07795.
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Nested filtering methods for Bayesian inference in state space models, PhD thesis, Sara Pérez Vieites, January 2022, Universidad Carlos III de Madrid.
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