Computer simulation of liquids
Computer simulation of liquids
Understanding Molecular Simulation: From Algorithms to Applications
Understanding Molecular Simulation: From Algorithms to Applications
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Variance reduction for particle filters of systems with time scale separation
IEEE Transactions on Signal Processing
Improved particle filters for multi-target tracking
Journal of Computational Physics
A random map implementation of implicit filters
Journal of Computational Physics
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This paper introduces a recursive particle filtering algorithm designed to filter high dimensional systems with complicated non-linear and non-Gaussian effects. The method incorporates a parallel marginalization (PMMC) step in conjunction with the hybrid Monte Carlo (HMC) scheme to improve samples generated by standard particle filters. Parallel marginalization is an efficient Markov chain Monte Carlo (MCMC) strategy that uses lower dimensional approximate marginal distributions of the target distribution to accelerate equilibration. As a validation the algorithm is tested on a 2516 dimensional, bimodal, stochastic model motivated by the Kuroshio current that runs along the Japanese coast. The results of this test indicate that the method is an attractive alternative for problems that require the generality of a particle filter but have been inaccessible due to the limitations of standard particle filtering strategies.