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We look at the task of estimating the parameters of a geometric constraint from noisy points in 2D. The classical approach of minimizing the Euclidean distance error between points and constraint generally yields biased estimates for nonlinear constraints and higher noise levels. To deal with this issue, the expected distribution of the distance error can be explicitly incorporated in the estimator...
In this paper, the problem of sparse nonparametric conditional density estimation based on samples and prior knowledge is addressed. The prior knowledge may be restricted to parts of the state space and given as generative models in form of mean-function constraints or as probabilistic models in the form of Gaussian mixture densities. The key idea is the introduction of additional constraints and...
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...
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