Particle filters

Abstract

This is a short review of Monte Carlo methods for approximating filter distributions in state space models. The basic algorithm and different strategies to reduce imbalance of the weights are discussed. Finally, methods for more difficult problems like smoothing and parameter estimation and applications outside the state space model context are presented.

Published in at http://dx.doi.org/10.3150/12-BEJSP07 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

References in corpus (4)

• Recursive Monte Carlo filters: Algorithms and theoretical analysis
• Sharp failure rates for the bootstrap particle filter in high dimensions
• Time series analysis via mechanistic models
• Genealogical particle analysis of rare events

Cited by in corpus (9)

• On Particle Methods for Parameter Estimation in State-Space Models
• Particle Filters and Data Assimilation
• On the role of interaction in sequential Monte Carlo algorithms
• Controlled Sequential Monte Carlo
• On the Rate of Abiogenesis from a Bayesian Informatics Perspective
• Sequential Monte Carlo as Approximate Sampling: bounds, adaptive resampling via $\infty$-ESS, and an application to Particle Gibbs
• Limit theorems for sequential MCMC methods
• Bellman filtering and smoothing for state-space models
• Physical ID-Transfer Attacks against Multi-Object Tracking via Adversarial Trajectory