toni/IPIN2016
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

near to final draft

Browse Source
This commit is contained in:
toni
2016-05-11 16:30:36 +02:00
parent 8c055bd71d
commit ff56649a5b
9 changed files with 23 additions and 44 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
tex/chapters/relatedwork.tex
View File

@@ -4,7 +4,7 @@
%kurze einleitung zum smoothing
Sequential MC filters, like the aforementioned particle filter, use all observations $\mObsVec_{1:t}$ until the current time $t$ for computing an estimation of the state $\mStateVec_t$.
In a Bayesian setting, this can be formalized as the computation of the posterior distribution $p(\mStateVec_t \mid \mObsVec_{1:t})$ using a sample of $N$ independent random variables, $\vec{X}^i_{t} \sim p(\mStateVec_t \mid \mObsVec_{1:t})$ for $i = 1,...,N$ for approximation.
In a Bayesian setting, this can be formalized as the computation of the posterior distribution $p(\mStateVec_t \mid \mObsVec_{1:t})$ using a sample set of $N$ independent random variables, $\vec{X}^i_{t} \sim p(\mStateVec_t \mid \mObsVec_{1:t})$ for $i = 1,...,N$ for approximation.
Due to importance sampling, a weight $W^i_t$ is assigned to each sample $\vec{X}^i_{t}$.
In the context of particle filtering $\{W^i_{1:t}, \vec{X}^i_{1:t} \}_{i=1}^N$ is a weighted set of samples, also called particles.
Therefore a particle is a representation of one possible system state $\mStateVec$.