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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 ...
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...
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...
OPTIMALITY OF THE AUXILIARY PARTICLE FILTER
Auxiliary particle filter central limit theorem adjustment multiplier weight
2009/9/18
In this article we study asymptotic properties of weighted
samples produced by the auxiliary particle filter (APF) proposed by Pitt
and Shephard [17]. Besides establishing a central limit theorem (C...