teaching/scientificComputing
Archived
3
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge branch 'master' of whale.am28.uni-tuebingen.de:scientificComputing

Browse Source
This commit is contained in:
Jan Grewe
2017-01-23 15:59:33 +01:00
parent 0ddc061d55 3c1de107fa
commit 4f3dd321f4
20 changed files with 1040 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
projects/project_random_walk/random_walk.tex
View File

@@ -41,7 +41,7 @@ in food gain the animal switches back to a random walk.
\begin{questions}
\question{} The accompanying dataset (random\_world.mat) contains a
single variable stored. This is the world (10000\,m$^2$ area with
single variable. This is the world (10000\,m$^2$ area with
10\,cm spatial resolution) in which there are randomly distributed
food sources (Gaussian blotches of food).
@@ -58,10 +58,12 @@ in food gain the animal switches back to a random walk.
with MATLAB)\\[0.5ex]
\part{} Same as above, but create a model animal that has some memory,
i.e. the direction is kept constant as long as there is a positive
gradient in the food gain. Otherwise a random walk is performed\\[0.5ex]
gradient in the food gain. Otherwise, a random walk is performed.\\[0.5ex]
\part{} Plot a typical example walk also for this agent.\\[0.5ex]
\part{} Compare the performance of the two agents. Create appropriate
plots and apply statistical methods.
\part{} Compare the performance of the two agents. Create
appropriate plots and apply statistical methods. You will need to
run the simulations several times to get a good estimate of the
neumbers.
\end{parts}
\end{questions}