FHWS/Fusion2018
Archived
4
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Fixed numbers in MISE eval

Browse Source
This commit is contained in:
Markus Bullmann
2018-05-08 11:52:08 +02:00
parent 9f358d69c9
commit 23b8f0886e
3 changed files with 5 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
tex/chapters/introduction.tex
View File

@@ -18,7 +18,7 @@ Additionally, in most practical scenarios the sample size, and hence the resolut
It is obvious, that a computation of the full posterior could solve the above, but finding such an analytical solution is an intractable problem, which is the reason for applying a sample representation in the first place.
Another promising way is to recover the probability density function from the sample set itself, by using a non-parametric estimator like a kernel density estimation (KDE).
With this, the \qq{real} most probable state is given by the maxima of the density estimation and thus avoids the aforementioned drawbacks.
With this, the \qq{real} most probable state is given by the maximum of the density estimation and thus avoids the aforementioned drawbacks.
However, non-parametric estimators tend to consume a large amount of computation time, which renders them unpractical for real time scenarios.
Nevertheless, the availability of a fast processing density estimate might improve the accuracy of today's sensor fusion systems without sacrificing their real time capability.