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When approximating one probability density with another density, it is desirable to minimize the information loss of the approximation as quantified by, e.g., the Kullback-Leibler divergence (KLD). It has been known for some time that in the case of the Gaussian distribution, matching the first two moments of the original density yields the optimal approximation in terms of minimizing the KLD. In...
Correlated uncertain angular quantities can be modeled using the bivariate wrapped normal distribution. In this paper, we focus on the problem of estimating the distribution's parameters from a given set of samples. For this purpose, we propose several new parameter estimation methods and compare them to estimation techniques found in literature. All methods are thoroughly evaluated in simulations...
In this paper, we address the problem of developing computationally efficient recursive estimators on the periodic domain of orientations using the Bingham distribution. The Bingham distribution is defined directly on the unit hypersphere. As such, it is able to describe both large and small uncertainties in a unified framework. In order to tackle the challenging computation of the normalization constant,...
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