搜索结果: 1-15 共查到“统计学 Filter”相关记录17条 . 查询时间(0.039 秒)
This paper presents a novel approach for constrained state estimation from noisy measurements. The optimal trending algorithms described in this paper assume that the trended system variables have the...
Covariance inflation in the ensemble Kalman filter: a residual nudging perspective and some implications
Covariance inflation ensemble Kalman filter residual nudging perspective some implications
2013/6/17
This note examines the influence of covariance inflation on the distance between the measured observation and the simulated (or predicted) observation with respect to the state estimate. In order for ...
The parameters of temporal models, such as dynamic Bayesian networks, may be modelled in a Bayesian context as static or atemporal variables that influence transition probabilities at every time step....
A multiple filter test for change point detection in renewal processes with varying variance
A multiple filter test change point detection renewal processes varying variance
2013/4/27
Non-stationarity of the event rate is a persistent problem in modeling time series of events, such as neuronal spike trains. Motivated by a variety of patterns in neurophysiological spike train record...
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...
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
Hidden Markov Models State-Space Models Sequential Monte Carlo
2011/7/5
Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models.
Data Driven Computing by the Morphing Fast Fourier Transform Ensemble Kalman Filter in Epidemic Spread Simulations
Data Driven Computing Morphing Fast Fourier Transform Ensemble Kalman Filter Epidemic Spread Simulations
2010/3/11
The FFT EnKF data assimilation method is proposed and applied to a stochastic
cell simulation of an epidemic, based on the S-I-R spread model. The FFT EnKF
combines spatial statistics and ensemble f...
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...
Estimating the Smoothing Parameter in the So-called Hodrick-Prescott Filter
adaptive estimation Hodrick-Prescott filter Kalman-Bucy Kalman filtering orthogonal parametrization, random walk, seasonal adjustment, spline state-space models time-series time-varying coefficients trend Whittaker-Henderson graduation
2009/3/9
This note gives a statistical description of the Hodrick-Prescott Filter (1997), originally proposed by Leser (1961). A maximum-likelihood estimator is derived and a related moments estimator is propo...
A Consistent Estimator of the Smoothing Parameter in the Hodrick-Prescott Filter
Adaptive estimation Gaussian process Hodrick-Prescott filter orthogonal parametrization
2009/3/5
The so-called Hodrick-Prescott filter was first introduced in actuarial science to estimate trends from claims data and now is widely used in economics and finance to estimate and predict e.g. busines...