resources.bib

@article{altekruger2023conditional,
  title = {Conditional Generative Models Are Provably Robust: {{Pointwise}} Guarantees for Bayesian Inverse Problems},
  author = {Altekr{\"u}ger, Fabian and Hagemann, Paul and Steidl, Gabriele},
  year = {2023},
  journal = {Transactions on Machine Learning Research},
  issn = {2835-8856},
  awesome-category = {method},
  awesome-tags = {diagnostics; theory},
  awesome-link-paper = {https://arxiv.org/abs/2303.15845}
}

@inproceedings{arruda2024anamortized,
  title = {An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation},
  author = {Arruda, Jonas and Sch{\"a}lte, Yannik and Peiter, Clemens and Teplytska, Olga and Jaehde, Ulrich and Hasenauer, Jan},
  booktitle = {Forty-first International Conference on Machine Learning},
  year = {2024},
  awesome-category = {method},
  awesome-tldr = {Neural posterior estimation for hierarchical models, where the NPE is used in a first stage on a local level and then repeatedly used for global inference leveraging amortization.},
  awesome-link-paper = {https://openreview.net/forum?id=uCdcXRuHnC},
  awesome-link-code = {https://github.com/arrjon/Amortized-NLME-Models/tree/ICML2024},
  awesome-tags = {parameter estimation; hierarchical models},
}

@misc{bahl2024advancing,
  title = {Advancing {{Tools}} for {{Simulation-Based Inference}}},
  author = {Bahl, Henning and Bres{\'o}, Victor and Crescenzo, Giovanni De and Plehn, Tilman},
  year = {2024},
  number = {arXiv:2410.07315},
  eprint = {2410.07315},
  primaryclass = {hep-ph},
  publisher = {arXiv},
  archiveprefix = {arXiv},
  awesome-category = {application},
  awesome-tags = {physics; simulation-based},
  awesome-link-paper = {https://arxiv.org/abs/2410.07315}
}

@article{bieringer2021measuring,
  title = {Measuring {{QCD}} Splittings with Invertible Networks},
  author = {Bieringer, Sebastian and Butter, Anja and Heimel, Theo and H{\"o}che, Stefan and K{\"o}the, Ullrich and Plehn, Tilman and Radev, Stefan T.},
  year = {2021},
  journal = {SciPost Physics},
  volume = {10},
  pages = {126},
  doi = {10.21468/SciPostPhys.10.6.126},
  awesome-category = {application},
  awesome-tags = {simulation-based; physics; parameter estimation}, 
  awesome-link-paper = {https://www.scipost.org/10.21468/SciPostPhys.10.6.126?acad_field_slug=astronomy}
}

@article{bischoff2024practical,
  title = {A Practical Guide to Sample-Based Statistical Distances for Evaluating Generative Models in Science},
  author = {Bischoff, Sebastian and Darcher, Alana and Deistler, Michael and Gao, Richard and Gerken, Franziska and Gloeckler, Manuel and Haxel, Lisa and Kapoor, Jaivardhan and Lappalainen, Janne K and Macke, Jakob H. and Moss, Guy and Pals, Matthijs and Pei, Felix C and Rapp, Rachel and Sa{\u g}tekin, A Erdem and Schr{\"o}der, Cornelius and Schulz, Auguste and Stefanidi, Zinovia and Toyota, Shoji and Ulmer, Linda and Vetter, Julius},
  year = {2024},
  journal = {Transactions on Machine Learning Research},
  issn = {2835-8856},
  awesome-category = {method},
  awesome-tags = {diagnostics; model evaluation}, 
  awesome-link-paper = {https://openreview.net/forum?id=isEFziui9p}
}

@misc{cannon2022investigating,
  title = {Investigating the {{Impact}} of {{Model Misspecification}} in {{Neural Simulation-based Inference}}},
  author = {Cannon, Patrick and Ward, Daniel and Schmon, Sebastian M.},
  year = {2022},
  number = {arXiv:2209.01845},
  eprint = {2209.01845},
  primaryclass = {stat},
  publisher = {arXiv},
  archiveprefix = {arXiv},
  awesome-category = {method},
  awesome-tags = {diagnostics; misspecification; simulation-based},
  awesome-link-paper = {https://arxiv.org/abs/2209.01845}
}

@article{cranmer2020frontier,
  title = {The Frontier of Simulation-Based Inference},
  author = {Cranmer, Kyle and Brehmer, Johann and Louppe, Gilles},
  year = {2020},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {117},
  number = {48},
  pages = {30055--30062},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1912789117},
  awesome-category = {review},
  langid = {english},
  awesome-link-paper = {https://www.pnas.org/doi/abs/10.1073/pnas.1912789117}
}

@article{dax2023neural,
  title = {Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference},
  author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and P{\"u}rrer, Michael and Wildberger, Jonas and Macke, Jakob H. and Buonanno, Alessandra and Sch{\"o}lkopf, Bernhard},
  year = {2023},
  journal = {Physical Review Letters},
  volume = {130},
  number = {17},
  pages = {171403},
  doi = {10.1103/PhysRevLett.130.171403},
  awesome-category = {method},
  awesome-link-paper = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.171403},
  awesome-tags = {likelihood-based; physics; parameter estimation}
}

@article{dingeldein2024amortized,
  title = {Amortized Template-Matching of Molecular Conformations from Cryo-Electron Microscopy Images Using Simulation-Based Inference},
  author = {Dingeldein, Lars and {Silva-S{\'a}nchez}, David and Evans, Luke and D'Imprima, Edoardo and Grigorieff, Nikolaus and Covino, Roberto and Cossio, Pilar},
  year = {2024},
  journal = {bioRxiv : the preprint server for biology},
  pages = {2024.07.23.604154},
  doi = {10.1101/2024.07.23.604154},
  awesome-category = {application},
  awesome-tags = {biology; simulation-based; parameter estimation},
  awesome-link-paper = {https://www.biorxiv.org/content/10.1101/2024.07.23.604154v2.abstract}

}

@article{elsemuller2024deep,
  title = {A Deep Learning Method for Comparing {{Bayesian}} Hierarchical Models.},
  author = {Elsem{\"u}ller, Lasse and Schnuerch, Martin and B{\"u}rkner, Paul-Christian and Radev, Stefan T.},
  year = {2024},
  journal = {Psychological Methods},
  issn = {1939-1463, 1082-989X},
  doi = {10.1037/met0000645},
  awesome-category = {method},
  awesome-tldr = {Proposes a multilevel neural architecture for compressing hierarchical data structures in Bayesian model comparison.},
  awesome-link-paper = {https://arxiv.org/abs/2301.11873},
  awesome-link-code = {https://github.com/bayesflow-org/Hierarchical-Model-Comparison},
  awesome-tags = {hierarchical models; model comparison; simulation-based}
}

@article{elsemuller2024sensitivityaware,
  title = {Sensitivity-Aware Amortized {{Bayesian}} Inference},
  author = {Elsem{\"u}ller, Lasse and Olischl{\"a}ger, Hans and Schmitt, Marvin and B{\"u}rkner, Paul-Christian and K{\"o}the, Ullrich and Radev, Stefan T.},
  year = {2024},
  journal = {Transactions on Machine Learning Research},
  issn = {2835-8856},
  awesome-category = {method},
  awesome-tldr = {Proposes a framework for amortized and thus efficient sensitivity analyses on all major dimensions of a Bayesian model.},
  awesome-link-paper = {https://openreview.net/forum?id=Kxtpa9rvM0},
  awesome-link-code = {https://github.com/bayesflow-org/SA-ABI},
  awesome-tags = {sensitivity analysis; simulation-based; meta learning}
}

@inproceedings{falkiewicz2023calibrating,
  title = {Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability},
  booktitle = {Thirty-Seventh Conference on Neural Information Processing Systems},
  author = {Falkiewicz, Maciej and Takeishi, Naoya and Shekhzadeh, Imahn and Wehenkel, Antoine and Delaunoy, Arnaud and Louppe, Gilles and Kalousis, Alexandros},
  year = {2023},
  awesome-category = {method},
  awesome-tags = {diagnostics; simulation-based},
  awesome-link-paper = {https://proceedings.neurips.cc/paper_files/paper/2023/file/03a9a9c1e15850439653bb971a4ad4b3-Paper-Conference.pdf}
}

@inproceedings{foster2021deep,
  title = {Deep {{Adaptive Design}}: {{Amortizing Sequential Bayesian Experimental Design}}},
  booktitle = {Proceedings of the 38th {{International Conference}} on {{Machine Learning}}},
  author = {Foster, Adam and Ivanova, Desi R. and Ilyas, Malik and Rainforth, Tom},
  year = {2021},
  volume = {139},
  publisher = {PMLR},
  awesome-category = {method},
  awesome-tags = {experimental design; adaptive design},
  awesome-link-paper = {http://proceedings.mlr.press/v139/foster21a.html}
}

@article{ghaderi-kangavari2023general,
  title = {A {{General Integrative Neurocognitive Modeling Framework}} to {{Jointly Describe EEG}} and {{Decision-making}} on {{Single Trials}}},
  author = {{Ghaderi-Kangavari}, Amin and Rad, Jamal Amani and Nunez, Michael D.},
  year = {2023},
  journal = {Computational Brain \& Behavior},
  volume = {6},
  number = {3},
  pages = {317--376},
  issn = {2522-0861, 2522-087X},
  doi = {10.1007/s42113-023-00167-4},
  awesome-category = {application},
  awesome-tags = {cognitive modeling; simulation-based; parameter estimation},
  awesome-link-paper = {https://link.springer.com/article/10.1007/s42113-023-00167-4}
}

@misc{habermann2024amortized,
  title = {Amortized {{Bayesian Multilevel Models}}},
  author = {Habermann, Daniel and Schmitt, Marvin and K{\"u}hmichel, Lars and Bulling, Andreas and Radev, Stefan T. and B{\"u}rkner, Paul-Christian},
  year = {2024},
  number = {arXiv:2408.13230},
  eprint = {2408.13230},
  publisher = {arXiv},
  archiveprefix = {arXiv},
  awesome-category = {method},
  awesome-tags = {parameter estimation; hierarchical models; simulation-based},
  awesome-link-paper = {https://arxiv.org/abs/2408.13230}
}

@article{heringhaus2022reliable,
  title = {Towards {{Reliable Parameter Extraction}} in {{MEMS Final Module Testing Using Bayesian Inference}}},
  author = {Heringhaus, Monika E. and Zhang, Yi and Zimmermann, Andr'e and Mikelsons, Lars},
  year = {2022},
  journal = {Sensors},
  volume = {22},
  number = {14},
  pages = {5408},
  issn = {1424-8220},
  doi = {10.3390/s22145408},
  awesome-category = {application},
  awesome-tags = {parameter estimation; simulation-based; engineering},
  awesome-link-paper = {https://www.mdpi.com/1424-8220/22/14/5408}
}

@misc{lavin2022simulation,
  title = {Simulation {{Intelligence}}: {{Towards}} a {{New Generation}} of {{Scientific Methods}}},
  shorttitle = {Simulation {{Intelligence}}},
  author = {Lavin, Alexander and Krakauer, David and Zenil, Hector and Gottschlich, Justin and Mattson, Tim and Brehmer, Johann and Anandkumar, Anima and Choudry, Sanjay and Rocki, Kamil and Baydin, At{\i}l{\i}m G{\"u}ne{\c s} and Prunkl, Carina and Paige, Brooks and Isayev, Olexandr and Peterson, Erik and McMahon, Peter L. and Macke, Jakob and Cranmer, Kyle and Zhang, Jiaxin and Wainwright, Haruko and Hanuka, Adi and Veloso, Manuela and Assefa, Samuel and Zheng, Stephan and Pfeffer, Avi},
  year = {2022},
  number = {arXiv:2112.03235},
  eprint = {2112.03235},
  primaryclass = {cs},
  publisher = {arXiv},
  archiveprefix = {arXiv},
  awesome-category = {review},
  awesome-link-paper = {https://arxiv.org/abs/2112.03235}
}

@misc{zammit2024neural,
  title={Neural Methods for Amortised Parameter Inference},
  author={Zammit-Mangion, Andrew and Sainsbury-Dale, Matthew and Huser, Rapha{\"e}l},
  year={2024},
  number={arXiv:2404.12484},
  eprint={2404.12484},
  primaryclass = {cs},
  publisher = {arXiv},
  archiveprefix = {arXiv},
  awesome-category = {review},
  awesome-link-paper = {https://arxiv.org/abs/2404.12484}
}

@inproceedings{moon2023amortized,
  title = {Amortized Inference with User Simulations},
  booktitle = {Proceedings of the 2023 {{CHI}} Conference on Human Factors in Computing Systems},
  author = {Moon, Hee-Seung and Oulasvirta, Antti and Lee, Byungjoo},
  year = {2023},
  series = {Chi '23},
  publisher = {Association for Computing Machinery},
  doi = {10.1145/3544548.3581439},
  awesome-category = {application},
  awesome-tags = {human-computer interaction, simulation-based; user interfaces},
  awesome-link-paper = {https://dl.acm.org/doi/pdf/10.1145/3544548.3581439}
}

@article{noever-castelos2022model,
  title = {Model Updating of Wind Turbine Blade Cross Sections with Invertible Neural Networks},
  author = {{Noever-Castelos}, Pablo and Ardizzone, Lynton and Balzani, Claudio},
  year = {2022},
  journal = {Wind Energy},
  volume = {25},
  number = {3},
  pages = {573--599},
  issn = {1095-4244, 1099-1824},
  doi = {10.1002/we.2687},
  awesome-category = {application},
  langid = {english},
  awesome-tags = {simulation-based},
  awesome-link-paper = {https://onlinelibrary.wiley.com/doi/full/10.1002/we.2687}
}

@article{papamakarios2021normalizing,
  title = {Normalizing Flows for Probabilistic Modeling and Inference},
  author = {Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji},
  year = {2021},
  journal = {Journal of Machine Learning Research},
  volume = {22},
  number = {57},
  pages = {1--64},
  awesome-category = {review},
  awesome-link-paper = {https://www.jmlr.org/papers/v22/19-1028.html}
}

@article{radev2021outbreakflow,
  title = {{{OutbreakFlow}}: {{Model-based Bayesian}} Inference of Disease Outbreak Dynamics with Invertible Neural Networks and Its Application to the {{COVID-19}} Pandemics in {{Germany}}},
  shorttitle = {{{OutbreakFlow}}},
  author = {Radev, Stefan T. and Graw, Frederik and Chen, Simiao and Mutters, Nico T. and Eichel, Vanessa M. and B{\"a}rnighausen, Till and K{\"o}the, Ullrich},
  editor = {Tanaka, Mark M.},
  year = {2021},
  journal = {PLOS Computational Biology},
  volume = {17},
  number = {10},
  pages = {e1009472},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1009472},
  awesome-category = {application},
  langid = {english},
  awesome-tags = {epidemiology; public health; simulation-based},
  awesome-link-paper = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009472}
}

@article{radev2020bayesflow,
  title = {{{BayesFlow}}: {{Learning Complex Stochastic Models With Invertible Neural Networks}}},
  shorttitle = {{{BayesFlow}}},
  author = {Radev, Stefan T. and Mertens, Ulf K. and Voss, Andreas and Ardizzone, Lynton and K{\"o}the, Ullrich},
  year = {2020},
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  volume = {33},
  number = {4},
  pages = {1452--1466},
  issn = {2162-237X, 2162-2388},
  doi = {10.1109/TNNLS.2020.3042395},
  awesome-category = {method},
  awesome-tags = {simulation-based; summary learning; parameter estimation},
  awesome-link-paper = {https://ieeexplore.ieee.org/abstract/document/9298920?casa_token=fdTVHBVa5Z4AAAAA:Un2ZODPlovOuoZZaCPLPrBwA58re3eYXPMbBx9u_WAj9PRUJj34W3hTEuSG1osciKgjzZpiS}
}

@article{radev2023amortized,
  title = {Amortized {{Bayesian Model Comparison With Evidential Deep Learning}}},
  author = {Radev, Stefan T. and D'Alessandro, Marco and Mertens, Ulf K. and Voss, Andreas and K{\"o}the, Ullrich and B{\"u}rkner, Paul-Christian},
  year = {2023},
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  volume = {34},
  number = {8},
  pages = {4903--4917},
  issn = {2162-237X, 2162-2388},
  doi = {10.1109/TNNLS.2021.3124052},
  awesome-category = {method},
  awesome-tags = {model comparison},
  awesome-link-paper = {https://ieeexplore.ieee.org/abstract/document/9612724?casa_token=knr0jyL2bTAAAAAA:Dh8KfjVW9QJympB0c8UbUq8HozJjOw-BPBSdy1g-QUhPskT1IL-cN5RHFHU7EVJNyZnY78Id}
}

@article{radev2023bayesflow,
  title = {{{BayesFlow}}: {{Amortized Bayesian}} Workflows with Neural Networks},
  author = {Radev, Stefan T. and Schmitt, Marvin and Schumacher, Lukas and Elsem{\"u}ller, Lasse and Pratz, Valentin and Sch{\"a}lte, Yannik and K{\"o}the, Ullrich and B{\"u}rkner, Paul-Christian},
  year = {2023},
  journal = {Journal of Open Source Software},
  volume = {8},
  number = {89},
  pages = {5702},
  publisher = {The Open Journal},
  doi = {10.21105/joss.05702},
  awesome-category = {software},
  awesome-link-paper = {https://joss.theoj.org/papers/10.21105/joss.05702},
  awesome-link-code = {https://github.com/bayesflow-org/bayesflow}
}

@inproceedings{radev2023jana,
  title = {{{JANA}}: {{Jointly}} Amortized Neural Approximation of Complex {{Bayesian}} Models},
  booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence},
  author = {Radev, Stefan T. and Schmitt, Marvin and Pratz, Valentin and Picchini, Umberto and K{\"o}the, Ullrich and B{\"u}rkner, Paul-Christian},
  editor = {Evans, Robin J. and Shpitser, Ilya},
  year = {2023},
  series = {Proceedings of Machine Learning Research},
  volume = {216},
  pages = {1695--1706},
  publisher = {PMLR},
  awesome-category = {method},
  awesome-tags = {joint learning; simulation-based; diagnostics},
  awesome-link-paper = {https://proceedings.mlr.press/v216/radev23a.html}
}

@article{sainsbury-dale2024likelihoodfree,
  title = {Likelihood-{{Free Parameter Estimation}} with {{Neural Bayes Estimators}}},
  author = {{Sainsbury-Dale}, Matthew and {Zammit-Mangion}, Andrew and Huser, Rapha{\"e}l},
  year = {2024},
  journal = {The American Statistician},
  volume = {78},
  number = {1},
  pages = {1--14},
  issn = {0003-1305, 1537-2731},
  doi = {10.1080/00031305.2023.2249522},
  awesome-category = {method},
  langid = {english},
  awesome-tags = {parameter estimation; point estimation; simulation-based},
  awesome-link-paper = {https://www.tandfonline.com/doi/full/10.1080/00031305.2023.2249522}
}

@misc{schmitt2023fuse,
  title = {Fuse {{It}} or {{Lose It}}: {{Deep Fusion}} for {{Multimodal Simulation-Based Inference}}},
  shorttitle = {Fuse {{It}} or {{Lose It}}},
  author = {Schmitt, Marvin and Radev, Stefan T. and B{\"u}rkner, Paul-Christian},
  year = {2023},
  number = {arXiv:2311.10671},
  eprint = {2311.10671},
  publisher = {arXiv},
  archiveprefix = {arXiv},
  awesome-category = {method},
  awesome-tags = {summary learning; parameter estimation},
  awesome-link-paper = {https://arxiv.org/abs/2311.10671}
}

@inproceedings{schmitt2024amortized,
  title = {Amortized {{Bayesian Workflow}} ({{Extended Abstract}})},
  booktitle = {{{NeurIPS Workshop}} on {{Bayesian Decision-Making}} and {{Uncertainty}}},
  author = {Schmitt, Marvin and Li, Chengkun and Vehtari, Aki and Acerbi, Luigi and B{\"u}rkner, Paul-Christian and Radev, Stefan T.},
  year = {2024},
  publisher = {arXiv},
  awesome-category = {method},
  awesome-tags = {likelihood-based; workflow},
  awesome-link-paper = {https://arxiv.org/abs/2409.04332}
}

@inproceedings{schmitt2024consistency,
  title = {Consistency {{Models}} for {{Scalable}} and {{Fast Simulation-Based Inference}}},
  booktitle = {Proceedings of the 38th International Conference on Neural Information Processing Systems},
  author = {Schmitt, Marvin and Pratz, Valentin and K{\"o}the, Ullrich and B{\"u}rkner, Paul-Christian and Radev, Stefan T.},
  year = {2024},
  awesome-category = {method},
  awesome-tags = {simulation-based},
  awesome-link-paper = {https://arxiv.org/abs/2312.05440}
}

@inproceedings{schmitt2024detecting,
  title = {Detecting Model Misspecification in Amortized {{Bayesian}} Inference with Neural Networks},
  booktitle = {Pattern Recognition},
  author = {Schmitt, Marvin and B{\"u}rkner, Paul-Christian and K{\"o}the, Ullrich and Radev, Stefan T.},
  year = {2024},
  pages = {541--557},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  awesome-category = {method},
  isbn = {978-3-031-54605-1},
  awesome-tags = {diagnostics; workflow},
  awesome-link-paper = {https://link.springer.com/chapter/10.1007/978-3-031-54605-1_35}
}

@inproceedings{schmitt2024leveraging,
  title = {Leveraging Self-Consistency for Data-Efficient Amortized {{Bayesian}} Inference},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning},
  author = {Schmitt, Marvin and Ivanova, Desi R. and Habermann, Daniel and K{\"o}the, Ullrich and B{\"u}rkner, Paul-Christian and Radev, Stefan T.},
  editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
  year = {2024},
  series = {Proceedings of Machine Learning Research},
  volume = {235},
  pages = {43723--43741},
  publisher = {PMLR},
  awesome-category = {method},
  awesome-tags = {likelihood-based; simulation-based},
  awesome-link-paper = {https://arxiv.org/abs/2310.04395}
}

@article{schumacher2023neural,
  title = {Neural Superstatistics for {{Bayesian}} Estimation of Dynamic Cognitive Models},
  author = {Schumacher, Lukas and B{\"u}rkner, Paul-Christian and Voss, Andreas and K{\"o}the, Ullrich and Radev, Stefan T.},
  year = {2023},
  journal = {Scientific Reports},
  volume = {13},
  number = {1},
  pages = {13778},
  issn = {2045-2322},
  doi = {10.1038/s41598-023-40278-3},
  awesome-category = {application},
  langid = {english},
  awesome-tags = {simulation-based; dynamic modeling; parameter estimation},
  awesome-link-paper = {https://www.nature.com/articles/s41598-023-40278-3}
}

@inproceedings{sharrock2024sequential,
  title = {Sequential Neural Score Estimation: {{Likelihood-free}} Inference with Conditional Score Based Diffusion Models},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning},
  author = {Sharrock, Louis and Simons, Jack and Liu, Song and Beaumont, Mark},
  editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
  year = {2024},
  series = {Proceedings of Machine Learning Research},
  volume = {235},
  pages = {44565--44602},
  publisher = {PMLR},
  awesome-category = {method},
  awesome-tags = {simulation-based},
  awesome-link-paper = {https://arxiv.org/abs/2210.04872}
}

@article{shiono2021estimation,
  title = {Estimation of Agent-Based Models Using {{Bayesian}} Deep Learning Approach of {{BayesFlow}}},
  author = {Shiono, Takashi},
  year = {2021},
  journal = {Journal of Economic Dynamics and Control},
  volume = {125},
  pages = {104082},
  issn = {01651889},
  doi = {10.1016/j.jedc.2021.104082},
  awesome-category = {application},
  langid = {english},
  awesome-tags = {agent modeling; simulation-based},
  awesome-link-paper = {https://www.sciencedirect.com/science/article/pii/S0165188921000178?casa_token=lM5bqeFY9-AAAAAA:usisG1ypAZdWNDwk39x_KdFGIvgKXoxYD9x0fukFWDyiqBEtXHaLPRIFhShjyeXdAmvoKgwNmA}
}

@article{siahkoohi2023reliable,
  title = {Reliable Amortized Variational Inference with Physics-Based Latent Distribution Correction},
  author = {Siahkoohi, Ali and Rizzuti, Gabrio and Orozco, Rafael and Herrmann, Felix J.},
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  journal = {GEOPHYSICS},
  volume = {88},
  number = {3},
  pages = {R297-R322},
  issn = {0016-8033, 1942-2156},
  doi = {10.1190/geo2022-0472.1},
  awesome-category = {application},
  langid = {english},
  awesome-tags = {physics; correction; misspecification},
  awesome-link-paper = {https://library.seg.org/doi/abs/10.1190/geo2022-0472.1}
}

@misc{starostin2024fast,
  title = {Fast and {{Reliable Probabilistic Reflectometry Inversion}} with {{Prior-Amortized Neural Posterior Estimation}}},
  author = {Starostin, Vladimir and Dax, Maximilian and Gerlach, Alexander and Hinderhofer, Alexander and {Tejero-Cantero}, {\'A}lvaro and Schreiber, Frank},
  year = {2024},
  number = {arXiv:2407.18648},
  eprint = {2407.18648},
  primaryclass = {cond-mat, physics:physics, stat},
  publisher = {arXiv},
  archiveprefix = {arXiv},
  awesome-category = {method},
  awesome-tags = {physics; meta learning},
  awesome-link-paper = {https://arxiv.org/abs/2407.18648}
}

@article{tejero-cantero2020sbi,
  title = {{sbi}: {{A}} Toolkit for Simulation-Based Inference},
  author = {{Tejero-Cantero}, Alvaro and Boelts, Jan and Deistler, Michael and Lueckmann, Jan-Matthis and Durkan, Conor and Gon{\c c}alves, Pedro J. and Greenberg, David S. and Macke, Jakob H.},
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  publisher = {The Open Journal},
  doi = {10.21105/joss.02505},
  awesome-category = {software},
  awesome-link-paper = {https://joss.theoj.org/papers/10.21105/joss.02505},
  awesome-link-code = {https://github.com/sbi-dev/sbi}
}

@inproceedings{tsilifis2022inverse,
  title = {Inverse {{Design}} under {{Uncertainty}} Using {{Conditional Normalizing Flows}}},
  booktitle = {{{AIAA SCITECH}} 2022 {{Forum}}},
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  year = {2022},
  publisher = {{American Institute of Aeronautics and Astronautics}},
  address = {San Diego, CA \& Virtual},
  doi = {10.2514/6.2022-0631},
  awesome-category = {application},
  isbn = {978-1-62410-631-6},
  langid = {english},
  awesome-tags = {engineering; aerospace; simulation-based},
  awesome-link-paper = {https://arc.aiaa.org/doi/abs/10.2514/6.2022-0631}
}

@article{vonkrause2022mental,
  title = {Mental Speed Is High until Age 60 as Revealed by Analysis of over a Million Participants},
  author = {{von Krause}, Mischa and Radev, Stefan T. and Voss, Andreas},
  year = {2022},
  journal = {Nature Human Behaviour},
  volume = {6},
  number = {5},
  pages = {700--708},
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  awesome-category = {application},
  langid = {english},
  awesome-tags = {cognitive modeling; parameter estimation},
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}

@inproceedings{ward2022robust,
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  booktitle = {Proceedings of the 36th International Conference on Neural Information Processing Systems},
  author = {Ward, Daniel and Cannon, Patrick and Beaumont, Mark and Fasiolo, Matteo and Schmon, Sebastian M.},
  year = {2022},
  awesome-category = {method},
  awesome-tags = {model evaluation; simulation-based},
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@article{wang2024missing,
  doi = {10.1371/journal.pcbi.1012184},
  author = {Wang, Zijian and Hasenauer, Jan and Schälte, Yannik},
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  publisher = {Public Library of Science},
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  awesome-tags = {missing data; simulation-based; parameter estimation}
}

@inproceedings{wehenkel2024simulationbased,
  title = {Simulation-Based Inference for Cardiovascular Models},
  booktitle = {{{NeurIPS}} Workshop},
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  year = {2024},
  awesome-category = {application},
  awesome-tags = {medicine; simulation-based; parameter estimation},
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}

@inproceedings{wildberger2023flow,
  title = {Flow Matching for Scalable Simulation-Based Inference},
  booktitle = {Advances in Neural Information Processing Systems},
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  awesome-category = {method},
  awesome-tags = {simulation-based},
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}

@article{zeng2023probabilistic,
  title = {Probabilistic Damage Detection Using a New Likelihood-Free {{Bayesian}} Inference Method},
  author = {Zeng, Jice and Todd, Michael D. and Hu, Zhen},
  year = {2023},
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  number = {2-3},
  pages = {319--341},
  issn = {2190-5452, 2190-5479},
  doi = {10.1007/s13349-022-00638-5},
  awesome-category = {application},
  langid = {english},
  awesome-tags = {structural health monitoring},
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}

@inproceedings{zhou2024evaluating,
  title = {Evaluating {{Sparse Galaxy Simulations}} via {{Out-of-Distribution Detection}} and {{Amortized Bayesian Model Comparison}}},
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  author = {Zhou, Lingyi and Radev, Stefan T. and Oliver, William H. and Obreja, Aura and Jin, Zehao and Buck, Tobias},
  year = {2024},
  awesome-category = {application},
  awesome-tags = {physics; model evaluation; model comparison},
  awesome-link-paper = {https://arxiv.org/abs/2410.10606}
}

@misc{chang2024amortized,
      title={Amortized Probabilistic Conditioning for Optimization, Simulation and Inference}, 
      author={Paul E. Chang and Nasrulloh Loka and Daolang Huang and Ulpu Remes and Samuel Kaski and Luigi Acerbi},
      year={2024},
      eprint={2410.15320},
      url={https://arxiv.org/abs/2410.15320}, 
      awesome-link-paper={https://arxiv.org/abs/2410.15320}, 
      awesome-link-code={https://github.com/acerbilab/amortized-conditioning-engine/},
      awesome-category = {method},
      awesome-tags = {meta-learning, optimization, simulation-based inference}
}