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Missing observations in stochastic difference equation with arma errorsMissing observations in stochastic difference equation with arma errors
(Sociedade Brasileira de Econometria, 1987)
Assimilation of ozone measurements in the air quality model AURORA by using the Ensemble Kalman Filter
(2011 50th IEEE Conference on Decision and Control and European Control Conference, 2020)
Inference of stochastic parametrizations for model error treatment using nested ensemble Kalman filters
(John Wiley & Sons Ltd, 2019-04)
Stochastic parametrizations are increasingly used to represent the uncertainty associated with model errors in ensemble forecasting and data assimilation. One of the challenges associated with the use of these parametrizations ...
Modeling and joint estimation of glottal source and vocal tract filter by state-space methods
(Elsevier, 2017-08)
Accurate estimation of the glottal source from a voiced sound is a difficult blind separation problem in speech signal processing. In this work, state-space methods are investigated to enhance the joint estimation of the ...
The Diffusion Kernel Filter
(SPRINGER, 2009)
A particle filter method is presented for the discrete-time filtering problem with nonlinear ItA ` stochastic ordinary differential equations (SODE) with additive noise supposed to be analytically integrable as a function ...
Early online detection of high volatility clusters using Particle Filters
(Elsevier, 2016-07)
This work presents a novel online early detector of high-volatility clusters based on uGARCH models (a variation of the GARCH model), risk-sensitive particle-filtering-based estimators, and hypothesis testing procedures. ...
Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods
(Taylor & Francis, 2018-01)
For modelling geophysical systems, large-scale processes are described through a set of coarse-grained dynamical equations while small-scale processes are represented via parameterizations. This work proposes a method for ...
Conditional filters for image sequence-based tracking - application to point tracking
(Institute of Electrical and Electronics Engineers, 2005-01)
A new conditional formulation of classical filtering methods is proposed. This formulation is dedicated to image sequence-based tracking. These conditional filters allow solving systems whose measurements and state ...
Robust insulin estimation under glycemic variability using Bayesian filtering and Gaussian process models
(Elsevier, 2018-04)
The ultimate goal of an artificial pancreas (AP) is finding the optimal insulin rates that can effectively reduce high blood glucose (BG) levels in type 1 diabetic patients. To achieve this, most autonomous closed-loop ...