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kde & moving avg draft

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Markus Bullmann
2018-02-11 22:19:58 +01:00
parent 651674d813
commit 4aa3ff5e30
6 changed files with 205 additions and 66 deletions
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tex/chapters/introduction.tex
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@@ -12,7 +12,8 @@ This value is then calculated by means of simple parametric point estimators, e.
While such methods are computational fast and suitable most of the time, it is not uncommon that they fail to recover the state in more complex scenarios.
Especially time-sequential, non-linear and non-Gaussian state spaces, depending upon a high number of different sensor types, frequently suffer from a multimodal representation of the posterior distribution.
As a result, those techniques are not able to provide an accurate statement about the most probable state, rather causing misleading or false outcomes.
For example in a localization scenario where a bimodal distribution represents the current posterior, a reliable position estimation is more likely to be at one of the modes, instead of somewhere in-between.
For example in a localization scenario where a bimodal distribution represents the current posterior, a reliable position estimation is more likely to be at one of the modes, instead of somewhere in-between.
\commentByMarkus{Vlt. noch drauf eingehen, dass avg. eben in die Mitte geht?}
Additionally, in most practical scenarios the sample size and therefore the resolution is limited, causing the variance of the sample based estimate to be high \cite{Verma2003}.
It is obvious, that a computation of the full posterior could solve the above, but finding such an analytical solution is an intractable problem, what is the reason for applying a sample representation in the first place.