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

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Theoretical Aspects of Spatial-Temporal Modeling

Part of the book series: SpringerBriefs in Statistics ((JSSRES))

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.

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Authors and Affiliations

  1. Institut Mines-Télécom/Télécom Lille/CRIStAL UMR 9189, Villeneuve D’ascq, France

    François Septier

  2. Department of Statistical Science, University College London, London, UK

    Gareth W. Peters

Authors
  1. François Septier
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  2. Gareth W. Peters
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Correspondence to François Septier .

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Editors and Affiliations

  1. Department of Statistical Science, University College London, London, United Kingdom

    Gareth William Peters

  2. The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan

    Tomoko Matsui

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Septier, F., Peters, G.W. (2015). An Overview of Recent Advances in Monte-Carlo Methods for Bayesian Filtering in High-Dimensional Spaces. In: Peters, G., Matsui, T. (eds) Theoretical Aspects of Spatial-Temporal Modeling. SpringerBriefs in Statistics(). Springer, Tokyo. https://doi.org/10.1007/978-4-431-55336-6_2

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