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An Overview of Recent Advances in Monte-Carlo Methods for Bayesian Filtering in High-Dimensional Spaces

  • François Septier
  • Gareth W. Peters
Chapter
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Abstract

Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the sequential Monte-Carlo (SMC) algorithm, also known as the particle filter. Nevertheless, this method tends to be inefficient when applied to high-dimensional problems. In this chapter, we present, an overview of recent contributions related to Monte-Carlo methods for sequential simulation from ultra high-dimensional distributions, often arising for instance in Bayesian applications.

Keywords

Particle Filter Importance Sampling Importance Weight Effective Sample Size Unscented Kalman Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© The Author(s) 2015

Authors and Affiliations

  1. 1.Institut Mines-Télécom/Télécom Lille/CRIStAL UMR 9189Villeneuve D’ascqFrance
  2. 2.Department of Statistical ScienceUniversity College LondonLondonUK

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