[18] D. Greenberg, M. Nonnenmacher, and J. Macke. Automatic posterior transformation for likelihood-free inference. In International Conference on Machine Learning, pages 2404-2414. PMLR, 2019.
[19] J. Hermans, V. Begy, and G. Louppe. Likelihood-free MCMC with amortized approximate ratio estimators. In International conference on machine learning, pages 4239-4248. PMLR, 2020.
[20] J. Hermans, A. Delaunoy, F. Rozet, A. Wehenkel, V. Begy, and G. Louppe. A crisis in simulation-based inference? Beware, your posterior approximations can be unfaithful. Transactions on Machine Learning Research, 2022.
[21] J. L. Horowitz. Ill-posed inverse problems in economics. Annu. Rev. Econ., 6(1):21–51, 2014.
[22] R. J. Hyndman. Computing and graphing highest density regions. The American Statistician, 50(2): 120-126, 1996.
[23] E. T. Jaynes. Prior information and ambiguity in inverse problems. Inverse problems, 14:151-166, 1984.
[24] D. P. Kingma and M. Welling. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114, 2013.
[25] G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter. Self-normalizing neural networks. Advances in neural information processing systems, 30, 2017.
[26] I. Kobyzev, S. J. Prince, and M. A. Brubaker. Normalizing flows: An introduction and review of current methods. IEEE transactions on pattern analysis and machine intelligence, 43(11):3964-3979, 2020.
[27] P. Lemos, A. Coogan, Y. Hezaveh, and L. Perreault-Levasseur. Sampling-based accuracy testing of posterior estimators for general inference. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, editors, Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 19256-19273. PMLR, 23-29 Jul 2023.
[28] J. Linhart, A. Gramfort, and P. L. Coelho Rodrigues. Validation Diagnostics for SBI algorithms based on Normalizing Flows. In NeurIPS 2022 Machine Learning and the Physical Sciences Workshop, pages 1-7, New Orleans, United States, Nov. 2022.
[29] T. Liu, P. Spincemaille, L. De Rochefort, B. Kressler, and Y. Wang. Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 61(1):196-204, 2009.
[30] J.-M. Lueckmann, P. J. Gonçalves, G. Bassetto, K. Öcal, M. Nonnenmacher, and J. H. Macke. Flexible statistical inference for mechanistic models of neural dynamics. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 1289-1299, 2017.
[31] J.-M. Lueckmann, J. Boelts, D. Greenberg, P. Goncalves, and J. Macke. Benchmarking simulation-based inference. In International Conference on Artificial Intelligence and Statistics, pages 343–351. PMLR, 2021.
[32] B. K. Miller, C. Weniger, and P. Forre. Contrastive neural ratio estimation. Advances in Neural Information Processing Systems, 35:3262-3278, 2022.
[33] Z. Pan, W. Yu, X. Yi, A. Khan, F. Yuan, and Y. Zheng. Recent progress on generative adversarial networks (GANs): A survey. IEEE access, 7:36322-36333, 2019.
[34] G. Papamakarios and I. Murray. Fast  $\varepsilon$ -free inference of simulation models with Bayesian conditional density estimation. Advances in neural information processing systems, 29, 2016.
[35] G. Papamakarios, D. Sterratt, and I. Murray. Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 837-848. PMLR, 2019.
[36] G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. The Journal of Machine Learning Research, 22(1):2617-2680, 2021.
[37] J. Pennington, S. Schoenholz, and S. Ganguli. The emergence of spectral universality in deep networks. In International Conference on Artificial Intelligence and Statistics, pages 1924-1932. PMLR, 2018.
[38] J. K. Pritchard, M. T. Seielstad, A. Perez-Lezaun, and M. W. Feldman. Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Molecular biology and evolution, 16(12): 1791-1798, 1999.