Improving Regularised Particle Filters

By C. Musso, N. Oudjane, F. Gland. Sequential Monte Carlo Methods in Practice, 2001

533 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/dbf14e53e0fbde9de226db5a73324434ac10c681

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. B. Vo
2. José Miguel Hernández-Lobato
3. Ba-Ngu Vo
4. B. Ristic
5. Alfonso Farina
6. Zoubin Ghahramani
7. Yarin Gal
8. Hwee Pink Tan

In Environmental Engineering

1. Hoshin V. Gupta
2. Soroosh Sorooshian
3. Hamid Moradkhani
4. Jasper A. Vrugt
5. K. Hsu
6. José Miguel Hernández-Lobato
7. B. Vo
8. Zoubin Ghahramani

In Statistical and Nonlinear Physics

1. José Miguel Hernández-Lobato
2. Dusist Niyato
3. B. Vo
4. Zoubin Ghahramani
5. Yue Wu
6. Hoshin V. Gupta
7. Marcos Eduardo Orchard
8. George J. Vachtsevanos

Citations in the corpus

• Particle Filtering in Geophysical Systems (2009, unknown): “An overview is given by Musso et al. (2001).”
• A particle-filtering approach for on-line fault diagnosis and failure prognosis (2009, methodology): “In consequence, considering all of the above, the regularization algorithm (Musso et al., 2001) when applied for long-term predictions is as follows:”
• Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications (2014, methodology): “This study focuses on the enhancement of progressive correction [109], which is a method for predicting samples from likelihoods to get closer to the posterior likelihood.”
• A Tutorial on Bernoulli Filters: Theory, Implementation and Applications (2013, methodology): “One technique we found particularly useful is known as importance sampling with progressive correction or tempering [56]–[58].”
• Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter (2005, background): “…importance resampling [Rubin, 1988; Smith and Gelfand, 1992], residual resampling [MacKay, 1992; Higuchi, 1997], auxiliary sampling importance resampling [Pitt and Shephard, 1999], minimum variance sampling [Doucet et al., 2001], and regularization based on kernel density [Musso et al., 2001].”
• Dynamic Covariance Models for Multivariate Financial Time Series (2013, methodology): “We analyzed several particle filtering methods, including Resample-Move (Gilks & Berzuini, 2001), Regularized Sequential Importance Sampling (Musso et al., 2001), Regularized Sequential Importance Resampling (Gordon et al., 1993), and Regularized Auxiliary Particle Filter (RAPF) (Liu & West, 1999)…”