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Particle filtering algorithm has been applied to various fields due to its capacity to handle nonlinear/non-Gaussian dynamic problems. One crucial issue in particle filtering is the selection of the proposal distribution that generates the particles. In this paper, we give a novel strategy for selecting proposal distribution. Firstly, divide-conquer(More)
A mixture Kalman Particle Filter (MKPF) based options pricing method is proposed. The MKPF algorithm uses the unscented Kalman filter (UKF) and the extended Kalman filter (EKF) as proposal distribution to generate the importance sampling density. Each particle is firstly updated by the UKF and obtains a state estimation. Thereafter, this estimation is used(More)
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