added readme

README.md

-# datainformed-prior
+# License
 
+ copyright Manuel Marschall (PTB) 2022
+
+ This software is licensed under the BSD-like license:
+
+ Redistribution and use in source and binary forms, with or without
+ modification, are permitted provided that the following conditions are met:
+
+ 1. Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+ 2. Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in
+    the documentation and/or other materials provided with the distribution.
+
+# DISCLAIMER
+
+ This software was developed at Physikalisch-Technische Bundesanstalt
+ (PTB). The software is made available "as is" free of cost. PTB assumes
+ no responsibility whatsoever for its use by other parties, and makes no
+ guarantees, expressed or implied, about its quality, reliability, safety,
+ suitability or any other characteristic. In no event will PTB be liable
+ for any direct, indirect or consequential damage arising in connection
+
+
+# Generative models and Bayesian inversion using Laplace approximation
+
+This project contains the implementation of the manuscript
+```
+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.
+
+# Code base
+
+To run the library, one needs a python 3.7 installation with the python packages given in the `requirements.txt`
+
+We propose to use an anaconda environment. In particular, clone the repository and use the following for installation:
+
+```
+conda create -n GenerativeBayes python==3.7
+pip install -r requirements.txt
+```
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