A Unifying Perspective on Probabilities as Model Predictions
Although probabilistic statements are ubiquitous, foundational disagreements persist about their understanding, as exemplified by debates between Bayesians and frequentists; moreover, it is unclear when and why acting on them actually leads to desirable outcomes. Here, we argue that every probability is the output of a \emph{prediction method}, that is, it depends on both a particular way of constructing abstractions and a way of transforming them into predictions. Through this, we provide a unifying perspective on supposedly different kinds of probabilities and show that even supposedly objective ones are model-dependent. We demonstrate that when a finite calibration criterion is met, on...
| Authors | Benedikt Höltgen |
|---|---|
| Primary category | stat.ML |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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