Add information about the preprint. (#18)

README.md

@@ -2,14 +2,31 @@
 
 # Label Shift
 
-Python library for *label shift* (known as prior probability shift, target shift) and *quantification* (estimating the class prevalences in an unlabeled data set under the prior probability shift assumption).
+Python library for *quantification* (estimating the class prevalence in an unlabeled data set) under the prior probability shift assumption.
+
 This module is created with two purposes in mind:
   - easily apply state-of-the-art quantification algorithms to the real problems,
   - benchmark novel quantification algorithms against others.
 
 It is compatible with any classifier using any machine learning framework.
 
-Contributions are very welcome! Please, check our [Contribution guide](CONTRIBUTING.md).
+The code inside was used to run the experiments in [our preprint](https://arxiv.org/abs/2302.09159), which can be cited as:
+```
+@misc{https://doi.org/10.48550/arxiv.2302.09159,
+  doi = {10.48550/ARXIV.2302.09159},
+  url = {https://arxiv.org/abs/2302.09159},
+  author = {Ziegler, Albert and Czyż, Paweł},
+  title = {Bayesian Quantification with Black-Box Estimators},
+  publisher = {arXiv},
+  year = {2023}
+}
+```
 
 ## Installation
 Currently the module is in early development stage and is not ready to be installed. It does not have proper documentation either. We hope to change it soon – thank you for your patience!
+
+## Contributions
+Contributions are very welcome! Please, check our [Contribution guide](CONTRIBUTING.md).
+
+
+