Results 1 to 10 of about 133,125 (287)
Multiplicative Kalman filtering [PDF]
TEST, 2010 We study a non-linear hidden Markov model, where the process of interest is the absolute value of a discretely observed Ornstein-Uhlenbeck diffusion, which is observed after a multiplicative perturbation. We obtain explicit formulae for the recursive relations which link the relevant conditional distributions.
Comte, Fabienne +2 more
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Autodifferentiable Ensemble Kalman Filters
SIAM Journal on Mathematics of Data Science, 2022 Data assimilation is concerned with sequentially estimating a temporally-evolving state. This task, which arises in a wide range of scientific and engineering applications, is particularly challenging when the state is high-dimensional and the state-space dynamics are unknown.
Yuming Chen +2 more
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Multimodal Kalman filtering [PDF]
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016 A difficult aspect of multimodal estimation is the possible discrepancy between the sampling rates and/or the noise levels of the considered data. Many algorithms cope with these dissimilarities empirically. In this paper, we propose a conceptual analysis of multimodality where we try to find the "optimal" way of combining modalities. More specifically,
Bourrier, Anthony +3 more
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Differentially private Kalman filtering [PDF]
2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2012 9 pages.
Ny, Jerome Le, Pappas, George J.
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Physica D: Nonlinear Phenomena, 2001 41 pages, 9 figures, correction of errors in the general multivariate ...
Sornette, Didier, Ide, Kayo
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Journal of Research of the National Bureau of Standards, 1985 The increased power of small computers makes the use of parameter estimation methods attractive. Such methods have a number of uses in analytical chemistry. When valid models are available, many methods work well, but when models used in the estimation are in error, most methods fail.
Brown, Steven D., Rutan, Sarah C.
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Journal of the Royal Statistical Society Series B: Statistical Methodology, 2000 Summary In treating dynamic systems, sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line ‘filtering’ task. We propose a special sequential Monte Carlo method, the mixture Kalman filter, which
Chen, Rong, Liu, Jun S.
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Deterministic Mean-field Ensemble Kalman Filtering [PDF]
, 2016 The proof of convergence of the standard ensemble Kalman filter (EnKF) from Legland etal. (2011) is extended to non-Gaussian state space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of
Law, Kody J. H. +2 more
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Kalman-Takens filtering in the presence of dynamical noise
, 2016 The use of data assimilation for the merging of observed data with dynamical models is becoming standard in modern physics. If a parametric model is known, methods such as Kalman filtering have been developed for this purpose.
Berry, Tyrus +2 more
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Linear filtering of systems with memory [PDF]
, 2004 We study the linear filtering problem for systems driven by continuous Gaussian processes with memory described by two parameters. The driving processes have the virtue that they possess stationary increments and simple semimartingale representations ...
Inoue, Akihiko +2 more
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