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In this paper, the estimation of conditional densities of continuous random variables from noisy samples is considered. The conditional densities are modeled as heteroscedastic Gaussian mixture densities allowing for closed-form solution of Bayesian inference with full densities. The key idea is a regularization based on the curvature of the conditional density function's surface. The main contributions...
In this paper, a distance-based method for both multivariate non-parametric density and conditional density estimation is proposed. The contributions are the formulation of both density estimation problems as weight optimization problems for Gaussian mixtures centered about samples with identical parameters. Furthermore, the minimization is based on the modified Cramér-von Mises distance of the Localized...
In this paper, a novel distance-based density estimation method is proposed, which considers the overall density function in the goodness-of-fit. In detail, the parameters of Gaussian mixture densities are estimated from samples, based on the distance of the cumulative distributions over the entire state space. Due to the ambiguous definition of the standard multivariate cumulative distribution, the...
This paper addresses the challenges of the fusion of two random vectors with imprecisely known stochastic dependency. This problem mainly occurs in decentralized estimation, e.g. of a distributed phenomenon, where the stochastic dependencies between the individual states are not stored. To cope with such problems we propose to exploit parameterized joint densities with both Gaussian marginals and...
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