Fix journal name

README.md

@@ -286,7 +286,7 @@ Learning both densities enables us to approximate marginal likelihoods or perfor
 Radev, S. T., Schmitt, M., Pratz, V., Picchini, U., Köthe, U., & Bürkner, P.-C. (2023).
 JANA: Jointly amortized neural approximation of complex Bayesian models.
 *Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, 216*, 1695-1706.
-([arXiv](https://arxiv.org/abs/2302.09125))([PLMR](https://proceedings.mlr.press/v216/radev23a.html))
+([arXiv](https://arxiv.org/abs/2302.09125))([PMLR](https://proceedings.mlr.press/v216/radev23a.html))
 
 ## Support
 This work is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy -– EXC-2181 - 390900948 (the Heidelberg Cluster of Excellence STRUCTURES) and -- EXC-2075 - 390740016 (the Stuttgart Cluster of Excellence SimTech), the Informatics for Life initiative funded by the Klaus Tschira Foundation, and Google Cloud through the Academic Research Grants program.
@@ -301,7 +301,7 @@ You can cite BayesFlow along the lines of:
 
 1. Radev, S. T., Schmitt, M., Schumacher, L., Elsemüller, L., Pratz, V., Schälte, Y., Köthe, U., & Bürkner, P.-C. (2023). BayesFlow: Amortized Bayesian workflows with neural networks. *arXiv:2306.16015*. ([arXiv](https://arxiv.org/abs/2306.16015))
 2. Radev, S. T., Mertens, U. K., Voss, A., Ardizzone, L., Köthe, U. (2020). BayesFlow: Learning complex stochastic models with invertible neural networks. *IEEE Transactions on Neural Networks and Learning Systems, 33(4)*, 1452-1466. ([arXiv](https://arxiv.org/abs/2003.06281))([IEEE TNNLS](https://ieeexplore.ieee.org/document/9298920))
-3. Radev, S. T., Schmitt, M., Pratz, V., Picchini, U., Köthe, U., & Bürkner, P.-C. (2023). JANA: Jointly amortized neural approximation of complex Bayesian models. *Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, 216*, 1695-1706. ([arXiv](https://arxiv.org/abs/2302.09125))([PLMR](https://proceedings.mlr.press/v216/radev23a.html))
+3. Radev, S. T., Schmitt, M., Pratz, V., Picchini, U., Köthe, U., & Bürkner, P.-C. (2023). JANA: Jointly amortized neural approximation of complex Bayesian models. *Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, 216*, 1695-1706. ([arXiv](https://arxiv.org/abs/2302.09125))([PMLR](https://proceedings.mlr.press/v216/radev23a.html))
 
 **BibTeX:**