%0 Generic %1 cranmer2019frontier %A Cranmer, Kyle %A Brehmer, Johann %A Louppe, Gilles %D 2019 %K review simulation-based_inference statistics %T The frontier of simulation-based inference %U http://arxiv.org/abs/1911.01429 %X Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving new momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound change these developments may have on science.

@misc{cranmer2019frontier, abstract = {Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving new momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound change these developments may have on science.}, added-at = {2020-04-21T19:33:38.000+0200}, author = {Cranmer, Kyle and Brehmer, Johann and Louppe, Gilles}, biburl = {https://www.bibsonomy.org/bibtex/25ae652b859207fb5e70117e02ff9ef73/peter.ralph}, interhash = {e905d0d8a50d32fd9ccd4b3334da9634}, intrahash = {5ae652b859207fb5e70117e02ff9ef73}, keywords = {review simulation-based_inference statistics}, timestamp = {2020-04-21T19:33:38.000+0200}, title = {The frontier of simulation-based inference}, url = {http://arxiv.org/abs/1911.01429}, year = 2019 }