通过可能性推理模型视角看归纳法与接续法则
Induction and the rule of succession through a possibilistic inferential model lens
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中文总结 AI 辅助
该研究从可能性推理模型（IM）视角探讨归纳法与接续法则，指出IM兼具独特统计可靠性且能抵御贝叶斯基础的批评，并通过日出问题对比其与拉普拉斯接续法则的解。
中文摘要 AI 辅助
归纳法是将经验证据转化为知识的过程。休谟曾提出，波普尔等人也认同，归纳法无法得到逻辑上的辩护。贝叶斯提出的较弱形式的归纳法，用概率来表述前述知识，我们回顾了对贝叶斯解的一些知名和不太知名的批评。随后我们研究了相对较新的可能性推理模型（IM）框架，表明除了IM拥有的独特强统计可靠性保证外，它还能抵御那些破坏贝叶斯基础的批评。为说明这一点，我们重新审视经典的日出问题，并将我们提出的解与拉普拉斯著名的接续法则进行比较。
英文摘要
Induction is the process by which empirical evidence is transformed to knowledge. Hume famously argued---and Popper and others agree---that there can be no logical justification for induction. A weaker form of induction, due to Bayes, expresses the aforementioned knowledge in terms of probabilities, and we review some well-known and not-so-well-known criticisms of the Bayesian solution. We then investigate the relatively new possibilistic inferential model (IM) framework, showing that, in addition to the IM's strong, statistical reliability guarantees that it uniquely enjoys, it is safe from those criticisms that damage the Bayesian foundations. For illustration, we reconsider the classical sunrise problem and compare our proposed solution with Laplace's famous rule of succession.
发表机构
- Purdue University（普渡大学）
- Wake Forest University（维克森林大学）
- Uppsala University（乌普萨拉大学）
- North Carolina State Univeristy（北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。
