adjusted readme

README.md

@@ -25,15 +25,14 @@
 
 # Generative models and Bayesian inversion using Laplace approximation
 
-This project contains the implementation of the manuscript
+This project contains the implementation for the examples in the [manuscript](http://arxiv.org/abs/2203.07755)
 ```
 M. Marschall, G. Wübbeler, F. Schmähling and C. Elster "Generative models and Bayesian inversion using Laplace approximation".
 ```
 
 # Contact and troubleshooting
 
-For direct contact, please write a quick mail to `manuel.marschall@ptb.de`.
-Alternativle, for problems regarding the code, its functionality oder bug-reports, please create an issue.
+For direct contact, please write a quick mail to `manuel.marschall@ptb.de` or create an issue.
 
 # Code base
 
@@ -44,4 +43,14 @@ We propose to use an anaconda environment. In particular, clone the repository a
 ```
 conda create -n GenerativeBayes python==3.7
 pip install -r requirements.txt
-```
\ No newline at end of file
+```
+
+# Run the code
+
+Simply call
+
+```
+python datainformed_prior/experiments_runner.py -n 100
+```
+
+Depending on your system, this might take a long time. With computations carried out on a Nvidia Tesla V100, the results are available in approximately 24h. Therefore, reduce the number of runs to `-n 3` for a smaller statistic. The final results are given in `paper_plots/`
\ No newline at end of file