Sequential Monte Carlo scheme for Bayesian estimation in the presence of data outliers.
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.
Department of Electrical Engineering, Arizona State University, Tempe, Arizona 85287, USA.
Liang
L
Huang
Ying-Cheng
YC
Lai