[53] Lueckmann, J.-M., Goncalves, P. J., Bassetto, G., Öcal, K., Nonnenmacher, M., and Macke, J. H. (2017). Flexible statistical inference for mechanistic models of neural dynamics. In Advances in Neural Information Processing Systems, volume 30.
[54] Mitrovic, J., Sejdinovic, D., and Teh, Y.-W. (2016). DR-ABC: Approximate Bayesian computation with kernel-based distribution regression. In Proceedings of the International Conference on Machine Learning, pages 1482-1491.
[55] Muandet, K., Fukumizu, K., Striperumbudur, B., and Scholkopf, B. (2017). Kernel mean embedding of distributions: A review and beyond. Foundations and Trends® in Machine Learning, 10(1-2):1-141.
[56] Niu, Z., Meier, J., and Briol, F.-X. (2021). Discrepancy-based inference for intractable generative models using quasi-Monte Carlo. arXiv:2106.11561.
[57] Pacchiardi, L. and Dutta, R. (2021). Generalized bayesian likelihood-free inference using scoring rules estimators. arXiv preprint arXiv:2104.03889.
[58] Pacchiardi, L. and Dutta, R. (2022). Likelihood-free inference with generative neural networks via scoring rule minimization. arXiv preprint arXiv:2205.15784.
[59] Papamakarios, G. and Murray, I. (2016). Fast  $\epsilon$ -free inference of simulation models with bayesian conditional density estimation. In International Conference on Neural Information Processing Systems, page 1036-1044.
[60] Papamakarios, G., Pavlakou, T., and Murray, I. (2017). Masked autoregressive flow for density estimation. Advances in neural information processing systems, 30.
[61] Papamakarios, G., Sterratt, D., and Murray, I. (2019). Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows. In International Conference on Artificial Intelligence and Statistics, volume 89, pages 837-848.
[62] Park, M., Jitkrittum, W., and Sejdinovic, D. (2015). K2-ABC: approximate Bayesian computation with kernel embeddings. International Conference on Artificial Intelligence and Statistics, 51:398–407.
[63] Pedersen, T. (2019). Stochastic multipath model for the in-room radio channel based on room electromagnetics. IEEE Transactions on Antennas and Propagation, 67(4):2591-2603.
[64] Pesonen, H., Simola, U., Kohn-Luque, A., Vuollekoski, H., Lai, X., Frigessi, A., Kaski, S., Frazier, D. T., Maneesoonthorn, W., Martin, G. M., and Corander, J. (2022). ABC of the future. International Statistical Review.
[65] Peter J. Huber, E. M. R. (2009). Robust Statistics. WILEY.
[66] Pomponi, J., Scardapane, S., and Uncini, A. (2021). Bayesian neural networks with maximum mean discrepancy regularization. Neurocomputing, 453:428-437.
[67] Price, L. F., Drovandi, C. C., Lee, A., and Nott, D. J. (2018). Bayesian synthetic likelihood. Journal of Computational and Graphical Statistics, 27(1):1-11.
[68] Pudlo, P., Marin, J.-M., Estoup, A., Cornuet, J.-M., Gautier, M., and Robert, C. P. (2015). Reliable ABC model choice via random forests. Bioinformatics, 32(6):859-866.
[69] Radev, S. T., Mertens, U. K., Voss, A., Ardizzone, L., and Kothe, U. (2022). Bayesflow: Learning complex stochastic models with invertible neural networks. IEEE Transactions on Neural Networks and Learning Systems, 33(4):1452-1466.
[70] Ramesh, P., Lueckmann, J.-M., Boelts, J., Tejero-Cantero, Á., Greenberg, D. S., Goncalves, P. J., and Macke, J. H. (2022). GATSBI: Generative adversarial training for simulation-based inference. In International Conference on Learning Representations.
[71] Riesselman, A. J., Ingraham, J. B., and Marks, D. S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15(10):816-822.