The particle filter is one of the most successful methods for state inference and identification of general non-linear and non-Gaussian models. However, standard particle filters suffer from degeneracy of the particle weights, in particular for high-dimensional problems. We propose a method for improving the performance of the particle filter for certain challenging state space models, with implications for high-dimensional inference. First we approximate the model by adding artificial process noise in an additional state update, then we design a proposal that combines the standard and the locally optimal proposal. This results in a bias-variance trade-off, where adding more noise reduces the variance of the estimate but increases the model...
We consider the problem of high-dimensional filtering of state-space models (SSMs) at discrete times...
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gauss...
The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However w...
The particle filter is one of the most successful methods for state inference and identification of ...
The state space model has been widely used in various fields including economics, finance, bioinform...
The particle filter provides a general solution to the nonlinear filtering problem with arbitrarily ...
Particle methods are a category of Monte Carlo algorithms that have become popular for performing in...
for performing inference in non-linear non-Gaussian state-space models. The class of “Rao-Blackwelli...
The state-space modeling of partially observed dynamical systems generally requires estimates of unk...
We present approximate algorithms for performing smoothing in a class of high-dimensional state-spac...
We present approximate algorithms for performing smoothing in a class of high-dimensional state-spac...
Abstract: We propose a novel method for maximum-likelihood-based parameter inference in nonlinear an...
We consider the numerical approximation of the filtering problem in high dimensions, that is, when t...
We propose particle filtering algorithms for tracking on infinite (or large) dimensional state space...
We study particle filtering algorithms for tracking on infinite (in practice, large) dimensional sta...
We consider the problem of high-dimensional filtering of state-space models (SSMs) at discrete times...
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gauss...
The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However w...
The particle filter is one of the most successful methods for state inference and identification of ...
The state space model has been widely used in various fields including economics, finance, bioinform...
The particle filter provides a general solution to the nonlinear filtering problem with arbitrarily ...
Particle methods are a category of Monte Carlo algorithms that have become popular for performing in...
for performing inference in non-linear non-Gaussian state-space models. The class of “Rao-Blackwelli...
The state-space modeling of partially observed dynamical systems generally requires estimates of unk...
We present approximate algorithms for performing smoothing in a class of high-dimensional state-spac...
We present approximate algorithms for performing smoothing in a class of high-dimensional state-spac...
Abstract: We propose a novel method for maximum-likelihood-based parameter inference in nonlinear an...
We consider the numerical approximation of the filtering problem in high dimensions, that is, when t...
We propose particle filtering algorithms for tracking on infinite (or large) dimensional state space...
We study particle filtering algorithms for tracking on infinite (in practice, large) dimensional sta...
We consider the problem of high-dimensional filtering of state-space models (SSMs) at discrete times...
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gauss...
The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However w...