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2021

Pérez-Vieites, Sara; Míguez, Joaquín

Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models Artículo de revista

En: Signal Processing, vol. 189, pp. 108295, 2021, ISSN: 0165-1684.

Resumen | Enlaces | BibTeX | Etiquetas: Bayesian inference, Filtering, Kalman, Monte Carlo, Parameter estimation

2018

Crisan, Dan; Míguez, Joaquín

Nested particle filters for online parameter estimation in discrete-time state-space Markov models Artículo de revista

En: Bernoulli, vol. 24, no. 4A, pp. 3039 – 3086, 2018.

Enlaces | BibTeX | Etiquetas: error bounds, model inference, Monte Carlo, Parameter estimation, Particle filtering, recursive algorithms, State space models

2015

Elvira, Victor; Martino, Luca; Luengo, David; Corander, Jukka

A Gradient Adaptive Population Importance Sampler Artículo en actas

En: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4075–4079, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8.

Resumen | Enlaces | BibTeX | Etiquetas: adaptive extensions, adaptive importance sampler, Adaptive importance sampling, gradient adaptive population, gradient matrix, Hamiltonian Monte Carlo, Hessian matrices, Hessian matrix, learning (artificial intelligence), Machine learning, MC methods, Monte Carlo, Monte Carlo methods, population Monte Carlo (PMC), proposal densities, Signal processing, Sociology, statistics, target distribution