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\begin{abstract}
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It is common practice to use a sample-based representation to solve problems having a probabilistic interpretation.
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In many real world scenarios one is then interested in finding a \qq{best estimate} of the underlying problem, \eg{} the position of a robot.
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In many real world scenarios one is then interested in finding a best estimate of the underlying problem, \eg{} the position of a robot.
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This is often done by means of simple parametric point estimators, providing the sample statistics.
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However, in complex scenarios this frequently results in a poor representation, due to multimodal densities and limited sample sizes.
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