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Fixed many bugs

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Markus Bullmann
2018-02-27 10:49:05 +01:00
parent 9d4927a365
commit 1fb9461a5f
8 changed files with 67 additions and 68 deletions
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tex/chapters/abstract.tex
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\begin{abstract}
It is common practice to use a sample-based representation to solve problems having a probabilistic interpretation.
In many real world scenarios one is then interested in finding a \qq{best estimate} of the underlying problem, e.g. the position of a robot.
This is often done by means of simple parametric point estimator, providing the sample statistics.
This is often done by means of simple parametric point estimators, providing the sample statistics.
However, in complex scenarios this frequently results in a poor representation, due to multimodal densities and limited sample sizes.
Recovering the probability density function using a kernel density estimation yields a promising approach to solve the state estimation problem i.e. finding the \qq{real} most probable state, but comes with high computational costs.