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

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


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.


Particle Filter Importance Sampling Importance Weight Effective Sample Size Unscented Kalman Filter 
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© 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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