Update README.md

Author: stefanradev93

README.md

@@ -291,17 +291,17 @@ available for free at: https://arxiv.org/abs/2302.09125
 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.
 
 
-# Citing BayesFlow
+## Citing BayesFlow
 
 You can cite BayesFlow along the lines of:
 
-- We estimated the approximate posterior distribution with neural posterior estimation and learned summary statistics (NPE; Radev et al., 2020) via the BayesFlow software for amortized Bayesian workflows (Radev et al., 2023b).
-- We trained a neural likelihood estimator (NLE; Papamakarios et al., 2019) via the BayesFlow software for amortized Bayesian workflows (Radev et al., 2023b).
-- We sampled from the approximate joint distribution $p(x, \theta)$ using jointly amortized neural approximation (JANA; Radev et al., 2023a), as implemented in the BayesFlow software for amortized Bayesian workflows (Radev et al., 2023b).
+- We approximated the posterior with neural posterior estimation and learned summary statistics (NPE; Radev et al., 2020), as implemented in the BayesFlow software for amortized Bayesian workflows (Radev et al., 2023b).
+- We approximated the likelihood with neural likelihood estimation (NLE; Papamakarios et al., 2019), as implemented in the BayesFlow software for amortized Bayesian workflows (Radev et al., 2023b).
+- We performed simultaneous posterior and likelihood estimation with jointly amortized neural approximation (JANA; Radev et al., 2023a), as implemented in the BayesFlow software for amortized Bayesian workflows (Radev et al., 2023b).
 
-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 paper](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 Trans Neural Netw Learn Syst. 33(4). 1452-1466.
-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. *39th conference on Uncertainty in Artificial Intelligence*. ([UAI Proceedings](https://openreview.net/forum?id=dS3wVICQrU0))
+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 Trans Neural Netw Learn Syst. 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))
 
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