TY - JOUR
T1 - Sequential Monte Carlo scheme for Bayesian estimation in the presence of data outliers
AU - Huang, Liang
AU - Lai, Ying-Cheng
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2007/5/21
Y1 - 2007/5/21
N2 - 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.
AB - 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.
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U2 - 10.1103/PhysRevE.75.056705
DO - 10.1103/PhysRevE.75.056705
M3 - Article
AN - SCOPUS:34347233509
SN - 1539-3755
VL - 75
JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
IS - 5
M1 - 056705
ER -