Probabilistic Acceptance
This addresses foundational problems in probabilistic reasoning and nonmonotonic logic for researchers in philosophy, AI, and statistics, but it appears incremental as it builds on existing debates without introducing a major new method.
The paper tackles the difficulties of fully accepting statements based on probability by proposing a contextual approach to full belief, showing that these issues are less severe than often portrayed and that the resulting structure naturally accommodates statistical inference.
The idea of fully accepting statements when the evidence has rendered them probable enough faces a number of difficulties. We leave the interpretation of probability largely open, but attempt to suggest a contextual approach to full belief. We show that the difficulties of probabilistic acceptance are not as severe as they are sometimes painted, and that though there are oddities associated with probabilistic acceptance they are in some instances less awkward than the difficulties associated with other nonmonotonic formalisms. We show that the structure at which we arrive provides a natural home for statistical inference.