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Toward Practical N2 Monte Carlo: the Marginal Particle Filter
Practical N2 Monte Carlo Marginal Particle Filter
2012/9/19
Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dy-namic models. These methods allow us to ap-proximate the joint posterior distribution using sequential ...
A two-stage denoising filter: the preprocessed Yaroslavsky filter
Image denoising Yaroslavsky lter Wavelets Curvelets Nonlocal Means
2012/9/18
This paper describes a simple image noise removal method which combines a preprocessing step with the Yaroslavsky lter for strong numerical, visual, and theoretical performance on a broad class of im...
Bridging the ensemble Kalman and particle filter
Bridging the ensemble Kalman particle filter
2012/9/17
In many applications of Monte Carlo nonlinear filtering, the propagation step is com-putationally expensive, and hence, the sample size is limited. With small sample sizes, the update step becomes cru...
A higher order correlation unscented Kalman filter
Sequential Parameter Estimation Nonlinear Systems Unscented Kalman Filter Continuous-discrete State Space Estimation of Uncorrelated States Volatility Estimation
2012/9/19
Many nonlinear extensions of the Kalman filter, e.g., the extended and the unscented Kalman filter, reduce the state densities to Gaussian densities. This approximation gives sufficient results in man...
Parameter estimation in the stochastic Morris-Lecar neuronal model with particle filter methods
Parameter estimatio stochastic Morris-Lecar neuronal mode particle filter methods
2012/9/19
In this paper, we consider the classic measurement error regression scenario in which our independent,or design, variables are observed with several sources of additive noise. We will show that our mo...
Comparison of SCIPUFF Plume Prediction with Particle Filter Assimilated Prediction for Dipole Pride 26 Data
Data Assimilation Particle Filter
2011/7/19
This paper presents the application of a particle filter for data assimilation in the context of puff-based dispersion models. Particle filters provide estimates of the higher moments, and are well su...