AIMar 27, 2013

Objective Probability

arXiv:1304.2727v12 citations
Originality Synthesis-oriented
AI Analysis

This addresses a foundational issue in probability theory for philosophers and statisticians, but it appears incremental as it builds on existing debates without introducing new empirical results.

The paper tackles the problem of distinguishing between statistical and subjective probabilities by arguing that the distinction based on unique versus repeatable events is untenable, and proposes a conception where all probabilities are based on statistical knowledge and every statement has a probability, applicable to rich languages.

A distinction is sometimes made between "statistical" and "subjective" probabilities. This is based on a distinction between "unique" events and "repeatable" events. We argue that this distinction is untenable, since all events are "unique" and all events belong to "kinds", and offer a conception of probability for A1 in which (1) all probabilities are based on -- possibly vague -- statistical knowledge, and (2) every statement in the language has a probability. This conception of probability can be applied to very rich languages.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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