Sequential Monte Carlo Methods for Optimal Filtering
Estimating the state of a nonlinear dynamic model sequentially in time is of paramount importance in applied science. Except in a few simple cases, there is no closed-form solution to this problem. It is therefore necessary to adopt numerical techniques in order to compute reasonable approximations. Sequential Monte Carlo (SMC) methods are powerful tools that allow us to accomplish this goal.
KeywordsMarkov Chain Monte Carlo Kalman Filter Extend Kalman Filter Importance Sampling Importance Weight
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