Amplification and Refinement of Bayesian Scientific Methodology

科学推理是从特殊事例到一般规律的过程，自亚里士多德时代以来一直是哲学关注的问题。然而，直到托马斯贝叶斯（1763年），才有任何明确的方法提出来使用概率论来证明从特殊到一般规律的推论。贝叶斯科学方法论的整个家族根据认知概率的概念来制定科学推理，认为科学假设的先验概率是一个有意义的概念，并利用贝叶斯定理来评估后验概率。1970年，Shimony教授提出了一个贝叶斯主义的版本，称为“缓和的个人主义”，建议个人概率评估通过对调查中所有认真提出的假设的开放态度来“缓和”。温和个人主义的优点之一是有可能为一个强有力的科学概括得出一个不可忽略的后验概率。然而，有一些弱点，在缓和个人主义，需要修改：特别是，难以证明公理的概率，的considereness“认真提出，”和不确定性的评价“捕捉所有”的假设。在这笔赠款下，Shimony教授与两名研究生合作，正在扩大和完善他原来的立场，以考虑到这些困难。他希望通过一个“不经锤炼的锤炼”的过程来解决第一个难题。他认为，第二和第三个困难可以通过适当地将概率的分析命题与后验原理相结合而得到改善。他和他的学生正在进行一些科学案例研究，目的是说明这种精炼方法的力量，并为归纳推理中制定后验原则提供理论依据。

Scientific inference, the process of going from particular instances to general laws, has been a matter of philosophical concern since the time of Aristotle. Not until Thomas Bayes (1763), however, was there any explicit method presented to use probability theory to justify the inference from particulars to general laws. The entire family of Bayesian scientific methodologies formulates scientific inference in terms of the concept of epistemic probability, considers the prior probability of scientific hypothesis to be a meaningful concept, and makes use of Bayes's theorem for evaluating posterior probabilities. A version of Bayesianism proposed by Professor Shimony in 1970, called "tempered personalism," recommends that personal probability evaluations be "tempered" by openmindedness towards all seriously proposed hypotheses in an investigation. Among the virtues of tempered personalism is the possibility of arriving at a non- negligible posterior probability for a strong scientific generalization. There are, however, some weaknesses in tempered personalism which require modifications: notably, a difficulty in justifying the axioms of probability, the vagueness of "seriously proposed," and uncertainty regarding the evaluation of the "catch- all" hypothesis. Under this grant, Professor Shimony, working with two graduate students, is amplifying and refining his original positions to take account of these difficulties. He expects to remove the first difficulty by a procedure of "tempering without tempering." He believes the second and third difficulties are ameliorated by properly combining analytic propositions of probability with a posteriori principles. He and his students are undertaking a number of scientific case studies for the purpose both of illustrating the power of this refined methodology and for heuristics in formulating the a posteriori principles in inductive inference.