Paper
21 May 2015 A baker’s dozen of new particle flows for nonlinear filters, Bayesian decisions and transport
Fred Daum, Jim Huang
Author Affiliations +
Abstract
We describe a baker’s dozen of new particle flows to compute Bayes’ rule for nonlinear filters, Bayesian decisions and learning as well as transport. Several of these new flows were inspired by transport theory, but others were inspired by physics or statistics or Markov chain Monte Carlo methods.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fred Daum and Jim Huang "A baker’s dozen of new particle flows for nonlinear filters, Bayesian decisions and transport", Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94740J (21 May 2015); https://doi.org/10.1117/12.2076201
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CITATIONS
Cited by 7 scholarly publications.
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KEYWORDS
Particles

Nonlinear filtering

Particle filters

Diffusion

Filtering (signal processing)

Algorithm development

Monte Carlo methods

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