{"title":"连贯更新信念度:激进概率、贝叶斯定理的推广及其对证据评价的影响","authors":"F. Taroni, Paolo Garbolino, S. Bozza","doi":"10.1093/LPR/MGAB001","DOIUrl":null,"url":null,"abstract":"\n The Bayesian perspective is based on conditioning related to reported evidence that is considered to be certain. What is called ‘Radical Probabilism’ replaces such an extreme view by introducing uncertainty on the reported evidence. How can such equivocal evidence be used in further inferences about a main hypothesis? The theoretical ground is introduced with the aim of offering to the readership an explanation for the generalization of the Bayes’ Theorem. This extension—that considers uncertainty related to the reporting of evidence—also has an impact on the assessment of the value of evidence through the Bayes’ factor. A generalization for such a logical measure of the evidence is also presented and justified.","PeriodicalId":48724,"journal":{"name":"Law Probability & Risk","volume":" ","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Coherently updating degrees of belief: Radical Probabilism, the generalization of Bayes’ Theorem and its consequences on evidence evaluation\",\"authors\":\"F. Taroni, Paolo Garbolino, S. Bozza\",\"doi\":\"10.1093/LPR/MGAB001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The Bayesian perspective is based on conditioning related to reported evidence that is considered to be certain. What is called ‘Radical Probabilism’ replaces such an extreme view by introducing uncertainty on the reported evidence. How can such equivocal evidence be used in further inferences about a main hypothesis? The theoretical ground is introduced with the aim of offering to the readership an explanation for the generalization of the Bayes’ Theorem. This extension—that considers uncertainty related to the reporting of evidence—also has an impact on the assessment of the value of evidence through the Bayes’ factor. A generalization for such a logical measure of the evidence is also presented and justified.\",\"PeriodicalId\":48724,\"journal\":{\"name\":\"Law Probability & Risk\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Law Probability & Risk\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1093/LPR/MGAB001\",\"RegionNum\":4,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"LAW\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Law Probability & Risk","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1093/LPR/MGAB001","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"LAW","Score":null,"Total":0}
Coherently updating degrees of belief: Radical Probabilism, the generalization of Bayes’ Theorem and its consequences on evidence evaluation
The Bayesian perspective is based on conditioning related to reported evidence that is considered to be certain. What is called ‘Radical Probabilism’ replaces such an extreme view by introducing uncertainty on the reported evidence. How can such equivocal evidence be used in further inferences about a main hypothesis? The theoretical ground is introduced with the aim of offering to the readership an explanation for the generalization of the Bayes’ Theorem. This extension—that considers uncertainty related to the reporting of evidence—also has an impact on the assessment of the value of evidence through the Bayes’ factor. A generalization for such a logical measure of the evidence is also presented and justified.
期刊介绍:
Law, Probability & Risk is a fully refereed journal which publishes papers dealing with topics on the interface of law and probabilistic reasoning. These are interpreted broadly to include aspects relevant to the interpretation of scientific evidence, the assessment of uncertainty and the assessment of risk. The readership includes academic lawyers, mathematicians, statisticians and social scientists with interests in quantitative reasoning.
The primary objective of the journal is to cover issues in law, which have a scientific element, with an emphasis on statistical and probabilistic issues and the assessment of risk.
Examples of topics which may be covered include communications law, computers and the law, environmental law, law and medicine, regulatory law for science and technology, identification problems (such as DNA but including other materials), sampling issues (drugs, computer pornography, fraud), offender profiling, credit scoring, risk assessment, the role of statistics and probability in drafting legislation, the assessment of competing theories of evidence (possibly with a view to forming an optimal combination of them). In addition, a whole new area is emerging in the application of computers to medicine and other safety-critical areas. New legislation is required to define the responsibility of computer experts who develop software for tackling these safety-critical problems.