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 (US)
Article number056705
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume75
Issue number5
DOIs
StatePublished - May 21 2007

Fingerprint

Sequential Monte Carlo
Bayesian Estimation
Outlier
Bayesian inference
biology
inference
Biology
disturbances
Monte Carlo Simulation
Disturbance
Physics
engineering
Engineering
physics
Experiment
Observation
simulation

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Mathematical Physics

Cite this

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