Title: Quasi-Monte Carlo Methods in Moderate Dimensions:
Chebyshev Lattices, Numerical Integration and Particle Filters
(Quasi-Monte Carlo technieken in matige dimensies: Chebyshev
roosterregels, numerieke integratie en deeltjesfilters) Other
Titles: Quasi-Monte Carlo Methods in Moderate Dimensions:
Chebyshev Lattices, Numerical Integration and Particle Filters
Authors: Poppe, Koen; S0107499 Issue Date: 12-Oct-2012
Abstract: In the first part, we detail our attempts to improve
the particle filter performance by replacing pseudo-random
numbers by quasi-random point sets. It is known from quasi-Monte
Carlo techniques that these point sets provide a better rate of
convergence in the context of numerical integration, however,
our experiments did not reveal the same superiority. We extend
the Kullback-Leibler divergence to the weighted particle sets
that are typically used in particle filters. This did not meet
our criteria for a quality measure and so the error on the
weighted average has been used. The experimen