Smoothing algorithms for state–space models
By Mark Briers, Arnaud Doucet, Simon Maskell. 2010
344 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/8b7ef67c9304a5a0c89eccaf052ea38c426fb7eb
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
- Adam M. Johansen
- Eric Moulines
- Arnaud Doucet
- Simo Särkkä
- Olivier Capp'e
- Sumeetpal S. Singh
- Simon J. Godsill
- Ajay Jasra
In Control and Systems Engineering
- Simo Särkkä
- Eric Moulines
- Simon J. Godsill
- Olivier Capp'e
- Sumeetpal S. Singh
- Adam M. Johansen
- Jan M. Maciejowski
- Bo Dai
Citations in the corpus
- An overview of sequential Monte Carlo methods for parameter estimation in general state-space models (2009, methodology): “To address this problem, a generalized version of the two-filter formula was proposed in [7].”
- System identification of nonlinear state-space models (2011, neutral): “tions of the smoothed state densities [6, 7, 14, 18, 39].”
- A Tutorial on Particle Filtering and Smoothing: Fifteen years later (2008, methodology): “To address this problem, a generalised version of the two-filter formula was proposed in [5].”
- Bayesian Filtering and Smoothing (2013, methodology): “It is indeed possible to form reasonable approximations by forming backward dynamic models by using, for example, the unscented transform (Wan and Van der Merwe, 2001), but in general this might not lead to good or valid approximations (Briers et al., 2010).”
- Learning Deep Hidden Nonlinear Dynamics from Aggregate Data (2018, methodology): “Finally the filtering estimates of both the forward and backward pass are combined into the smoothed estimates (Briers et al., 2010).”
- Filtering via approximate Bayesian computation (2010, positive): “Finally, any task which is feasible via SMC (e.g. smoothing—see Briers et al. 2010) can be achieved for the ABC approximation.”
- An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, methodology): “area. Some restrictive assumptions in the two-filter smoothers have been removed, and schemes for making them computationally more tractable have been introduced in [ 72 ], [73].”