21 May 2015 A baker’s dozen of new particle flows for nonlinear filters, Bayesian decisions and transport
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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.
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Fred Daum, Fred Daum, Jim Huang, 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); doi: 10.1117/12.2076201; https://doi.org/10.1117/12.2076201
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