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Vectorized and parallel particle filter SMC parameter estimation for stiff ODEs

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  • Particle filter (PF) sequential Monte Carlo (SMC) methods are very attractive for estimating parameters of time-dependent systems where the data is either not all available at once, or the range of time constants is wide enough to create problems in the numerical time propagation of the states. The need to evolve (and hence integrate) a large number of particles makes PF-based methods computationally challenging, and parallelization is often advocated to speed up computing time. While careful parallelization may indeed improve performance, vectorization of the algorithm on a single processor may result in even larger speedups for certain problems. In this paper we demonstrate how the PF-SMC class of algorithms proposed in [2] can be implemented in both parallel and vectorized computing environments, illustrating the performance with computed examples in MATLAB. In particular, two stiff test problems with different features show that both the size and structure of the problem affect which version of the algorithm is more efficient.
    Mathematics Subject Classification: Primary: 65Y05, 65Y10; Secondary: 62M20, 65L06, 62M05.


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