Sequential Monte Carlo scheme for Bayesian estimation in the presence of data outliers

Liang Huang, Ying-Cheng Lai

Research output: Contribution to journalArticle

Abstract

Bayesian inference has been used widely in physics, biology, and engineering for a variety of experiment- or observation-based estimation problems. Sequential Monte Carlo simulations are effective for realizing Bayesian estimations when the system and observational processes are nonlinear. In realistic applications, large disturbances in the observation, or outliers, may be present. We develop a theory and practical strategy to suppress the effect of outliers in the experimental observation and provide numerical support.

Original languageEnglish
Article number056705
Number of pages6
JournalPhysical Review. E, Statistical, Nonlinear and Soft Matter Physics
Volume75
Issue number5
DOIs
Publication statusPublished - 21 May 2007

Keywords

  • state-space models

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