Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

We review some advances of the particle filtering (PF) algorithm that have been achieved in
the last decade in the context of target tracking, with regard to either a single target or …

We consider the generic problem of performing sequential Bayesian inference in a state
space model with observation process y, state process x and fixed parameter θ. An idealized …

A core problem in statistics and probabilistic machine learning is to compute probability
distributions and expectations. This is the fundamental problem of Bayesian statistics and …

摘要本文梳理了粒子滤波理论基本内容, 发展脉络和最新研究进展, 特别是对其在多目标跟踪
应用中的一系列难点问题与主流解决思路进行了详细分析和报道. 常规粒子滤波研究重点主要 …

We investigate the stability of a Sequential Monte Carlo (SMC) method applied to the
problem of sampling from a target distribution on R^d for large d. It is well known Bengtsson …

This tutorial aims to provide an accessible introduction to particle filters, and sequential
Monte Carlo (SMC) more generally. These techniques allow for Bayesian inference in …

Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems.
Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well …

Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs
samplers Page 1 Bernoulli 24(2), 2018, 842–872 DOI: 10.3150/15-BEJ785 Uniform ergodicity of …

Resampling is a critical procedure that is of both theoretical and practical significance for
efficient implementation of the particle filter. To gain an insight of the resampling process …