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Particle filter (PF) sequential Monte Carlo (SMC) methods are very attractivefor the estimation of parameters of time dependent systems where the data iseither not all available at once, or the range of time constants is wide enoughto create problems in the numerical time propagation of the states. The need toevolve a large number of particles makes PF-based methods computationallychallenging, the main bottlenecks being the time propagation of each particleand the large number of particles. While parallelization is typically advocatedto speed up the computing time, vectorization of the algorithm on a singleprocessor may result in even larger speedups for certain problems. In thispaper we present a formulation of the PF-SMC class of algorithms proposed inArnold et al. (2013), which is particularly amenable to a parallel orvectorized computing environment, and we illustrate the performance with a fewcomputed examples in MATLAB.

Vectorized and Parallel Particle Filter SMC Parameter Estimation for Stiff ODEs