A Unifying Perspective on Probabilities as Model Predictions

Abstract: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, one can anticipate the distribution of utilities for a given policy and inform successful decision-making on finite sets of events. Based on the notion of prediction methods, inductive arguments, and the probability calculus, we explain the feasibility of the calibration criterion in many settings. Overall, we develop a coherent perspective on probabilities and their use, connecting key intuitions behind other interpretations along the way.

Machine Learning (stat.ML); Computers and Society (cs.CY); Machine Learning (cs.LG); Statistics Theory (math.ST)

arXiv:2609.09855 [stat.ML]

(or arXiv:2609.09855v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.09855 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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