Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter

29 May 2018Manuel PulidoPeter Jan vanLeeuwen

In this work, a novel sequential Monte Carlo filter is introduced which aims at efficient sampling of high-dimensional state spaces with a limited number of particles. Particles are pushed forward from the prior to the posterior density using a sequence of mappings that minimizes the Kullback-Leibler divergence between the posterior and the sequence of intermediate densities... (read more)

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