We show an important property of the von Mises distribution on the unit circle. If we approximate an arbitrary circular distribution using a von Mises distribution, the result obtained by trigonometric moment matching also minimizes the Kullback-Leibler divergence (Theorem 1). This result is a justification for circular filtering algorithms based on trigonometric moment matching as the loss of information is minimized. Furthermore, we show that Theorem 1 does not hold for the wrapped normal distribution.
Trigonometric moment matching and minimization of the Kullback-Leibler divergence
IEEE Transactions on Aerospace and Electronic Systems ; 51 , 4 ; 3480-3484
2015-10-01
208658 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Minimization of the Kullback-Leibler Divergence for Nonlinear Estimation
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|Minimization of the Kullback-Leibler Divergence for Nonlinear Estimation
Online Contents | 2017
|Minimization of the Kullback-Leibler Divergence for Nonlinear Estimation
Online Contents | 2017
|