Reality gap

The difference between the real world and the simulations we use to model it

February 21, 2024 — February 21, 2024

approximation
Bayes
feature construction
likelihood free
machine learning
measure
metrics
probability
sciml
statistics
time series
Figure 1

Somewhere between approximating simulators and calibrating simulators, inference using simulators, and parameter search we might wonder about what the gap is between them and the reality they purport to model, and this gap might be an object of interest in itself.

This is a page to think about that.

1 References

Brehmer, Louppe, Pavez, et al. 2020. “Mining Gold from Implicit Models to Improve Likelihood-Free Inference.” Proceedings of the National Academy of Sciences.
Higdon, Gattiker, Williams, et al. 2008. “Computer Model Calibration Using High-Dimensional Output.” Journal of the American Statistical Association.
Kennedy, and O’Hagan. 2001. “Bayesian Calibration of Computer Models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology).
Koermer, Loda, Noble, et al. 2023. “Active Learning for Simulator Calibration.”
Plumlee. 2017. “Bayesian Calibration of Inexact Computer Models.” Journal of the American Statistical Association.
Snoek, Larochelle, and Adams. 2012. “Practical Bayesian Optimization of Machine Learning Algorithms.” In Advances in Neural Information Processing Systems.