PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractParticle filters are a data assimilation technique that estimate a posterior distribution given a set of observations. Particle filters often use samplers that produce independent, weighted samples. We analyze two implicit filters; the first is a linear map and second is a symmetrized linear map. Then we study three particle filters on a one dimensional example and a three dimensional system of ordinary di↵erential equations. The implicit sampler on a symmetrized linear map reduces the variance in weights compared to the implicit sampler on a linear map. By comparing these methods on specific examples, we do not find distinctive di↵erence between the three filters, though the bootstrap filter does show marginally better results than the others.
Degree ProgramHonors College