Sara Pérez Vieites Postdoctoral Researcher

Publications

Journal publications

[J2]Pérez-Vieites, S., & Míguez, J. (2021). Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models. Signal Processing, 189, 108295.
[DOI] [arXiv] [code]

[J1]Pérez-Vieites, S., Mariño, I. P., & Míguez, J. (2018). Probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems. Physical Review E, 98(6), 063305.
[DOI] [arXiv] [poster] [slides] [code]


Conference papers

[C6].  Cox, B., Pérez-Vieites, S., Zilberstein, N., Sevilla, M., Segarra, S., & Elvira, V. (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.
[DOI] [pdf]

[C5].  Sevilla, M., Zilberstein, N., Cox, B., Pérez-Vieites, S., Elvira, V., & Segarra, S. (2023). State and Dynamics Estimation with the Kalman–Langevin filter. In 2023 57th Asilomar Conference on Signals, Systems, and Computers (pp. 1372-1376). IEEE.
[DOI] [pdf]

[C4]Pérez-Vieites, S., & Elvira, V. (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.
[DOI] [pdf] [poster] [video]

[C3]Pérez-Vieites, S., & Míguez, J. (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.
[DOI] [pdf] [slides] [video]

[C2]Pérez-Vieites, S., & Míguez, J. (2020). Kalman-based nested hybrid filters for recursive inference in state-space models. In 2020 28th European Signal Processing Conference (EUSIPCO) (pp. 2468-2472). IEEE.
[DOI] [pdf]

[C1]Pérez-Vieites, S., Vilà-Vals, J., Bugallo, M. F., Míguez, J., & Closas, P. (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.
[DOI] [pdf]


Pre-prints

Pérez-Vieites, S., Molina-Bulla, H., & Míguez, J. (2022). Nested smoothing algorithms for inference and tracking of heterogeneous multi-scale state-space systems. arXiv preprint arXiv:2204.07795.
[DOI] [arXiv] [poster]


Ph.D. Thesis

Nested filtering methods for Bayesian inference in state space models, PhD thesis, Sara Pérez Vieites, January 2022, Universidad Carlos III de Madrid.
[pdf] [slides]