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 language||English (US)|
|Journal||Physical Review E - Statistical, Nonlinear, and Soft Matter Physics|
|State||Published - May 21 2007|
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Statistics and Probability
- Condensed Matter Physics