In an age of expensive experiments and hype around new data-driven methods, researchers understandably want to ensure they are gleaning as much insight from their data as possible. Rachel C. Kurchin argues that there is still plenty to be learned from older approaches without turning to black boxes.
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Acknowledgements
The author gratefully acknowledges J. Wang, J. Freudenburg, and P. Komiske for helpful conversations, suggestions and topical references.
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Kurchin, R.C. Using Bayesian parameter estimation to learn more from data without black boxes. Nat Rev Phys 6, 152–154 (2024). https://doi.org/10.1038/s42254-024-00698-0
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DOI: https://doi.org/10.1038/s42254-024-00698-0
