We prove a theorem that guarantees the existence of a particle flow corresponding to Bayes’ rule, assuming certain regularity conditions (smooth and nowhere vanishing probability densities). This theory applies to particle flows to compute Bayes’ rule for nonlinear filters, Bayesian decisions and learning as well as transport. The particle flow algorithms reduce computational complexity by orders of magnitude compared with standard Markov chain Monte Carlo (MCMC) algorithms that achieve the same accuracy for high dimensional problems.

Date Published: 21 May 2015
PDF: 10 pages
Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94740I (21 May 2015); doi: 10.1117/12.2076167

Published in SPIE Proceedings Vol. 9474:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV
Ivan Kadar, Editor(s)