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Lookahead in Sequential Monte Carlo

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Dynamic systems and state space models are widely used in many fields of applications. Most of them are nonlinear and non-Gaussian. Sequential Carlo methods (Monte Carlo filters) have been developed for dealing with such systems efficiently. It is noticed that most of the dynamic systems process strong memory. In such systems, future observations often contain significant amount of information on the current system state. Hence, lookahead (estimation with future observations) can often produce more accurate inferences on the current state. In this talk we present several efficient lookahead Sequential Monte Carlo algorithms that utilize the future information with manageable computational cost. Some theoretical justifications and applications in signal processing will be presented.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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