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Editors: Sequential Monte Carlo Methods in Practice

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The article was published on 2001-01-01 and is currently open access. It has received 1215 citations till now. The article focuses on the topics: Dynamic Monte Carlo method & Monte Carlo method in statistical physics.

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Bandwidth Selection In Pre-Smoothed Particle Filters

TL;DR: In this paper, the authors apply a kernel smoother to the particles in the standard SIR filter for non-linear state space models with additive Gaussian observation noise, which reduces the Monte Carlo error in the estimates of both the posterior density of the states and the marginal density of observations at each time point.
Proceedings ArticleDOI

Proposal distribution for particle filtering applied to terrain navigation

TL;DR: The suggested method is based on the use of an importance distribution centered around an estimate of the maximum a posteriori (MAP) and it is shown that the computation of the MAP can be reduced to an optimization problem in a space of lower state dimension.

Estimation, Decision and Applications to Target Tracking

Yu Liu
TL;DR: A joint performance measure is proposed for JDE algorithms for dynamic problems where data is made available sequentially and the requirement of identical distribution is too stringent for many applications.
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Efficient nonparametric importance sampling for Bayesian learning

TL;DR: This paper proposes some modifications to existing adaptive importance sampling algorithms, which produce significantly more accurate estimates, and presents a first attempt at theoretical analysis of the sampling algorithms.
Proceedings ArticleDOI

Adaptive Sensing of Dynamic Target State in Heavy Sea Clutter

TL;DR: The method involves vectorization of the equations for the dynamical system model governing the temporal evolution of the clutter matrix followed by a multiple particle filtering approach to deal with the high dimensionality of the formulation.
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