http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 online resource (pages 194-595) : illustrations (black and white)
"This book introduces readers to the fundamentals of Bayesian epistemology. It begins by motivating and explaining the idea of a degree of belief (also known as a “credence”). It then presents Bayesians' five core normative rules governing degrees of belief: Kolmogorov's three probability axioms, the Ratio Formula for conditional credences, and Conditionalization for updating credences over time. After considering a few proposed additions to these norms, it applies the core rules to confirmation and decision theory. The book then details arguments for the Bayesian rules based on representation theorems, Dutch Books, and accuracy measures. Finally, it looks at objections and challenges to Bayesian epistemology. It presents problems concerning memory loss, self-location, old evidence, logical omniscience, and the subjectivity of priors. It considers the rival statistical paradigms of frequentism and likelihoodism. Then it explores alternative Bayesian-style formalisms involving comparative confidence rankings, credences ranges, and Dempster-Shafer functions"--Publisher's description.
Cover -- Fundamentals of Bayesian Epistemology 2: Arguments, Challenges, Alternatives -- Copyright -- Contents -- Quick Reference -- PART III: APPLICATIONS -- 6: Confirmation -- 6.1 Formal features of the confirmation relation -- 6.1.1 Confirmation is weird! The Paradox of the Ravens -- 6.1.2 Further adequacy conditions -- 6.2 Carnap's theory of confirmation -- 6.2.1 Confirmation as relevance -- 6.2.2 Finding the right function -- 6.3 Grue -- 6.4 Subjective Bayesian confirmation -- 6.4.1 Confirmation measures -- 6.4.2 Subjective Bayesian solutions to the Paradox of the Ravens -- 6.5 Exercises -- 6.6 Further reading -- Notes -- 7: Decision Theory -- 7.1 Calculating expectations -- 7.1.1 The move to utility -- 7.2 Expected utility theory -- 7.2.1 Preference rankings and money pumps -- 7.2.2 Savage's expected utility -- 7.2.3 Jeffrey's theory -- 7.2.4 Risk aversion and Allais' Paradox -- 7.3 Causal Decision Theory -- 7.3.1 Newcomb's Problem -- 7.3.2 A causal approach -- 7.3.3 Responses and extensions -- 7.4 Exercises -- 7.5 Further reading -- Notes -- Further Reading -- PART IV: ARGUMENTS FOR BAYESIANISM -- 8: Representation Theorems -- 8.1 Ramsey's four-step process -- 8.2 Savage's representation theorem -- 8.3 Representation theorems and probabilism -- 8.3.1 Objections to the argument -- 8.3.2 Reformulating the argument -- 8.4 Exercises -- 8.5 Further reading -- Notes -- 9: Dutch Book Arguments -- 9.1 Dutch Books -- 9.1.1 Dutch Books for probabilism -- 9.1.2 Further Dutch Books -- 9.2 The Dutch Book Argument -- 9.2.1 Dutch Books depragmatized -- 9.3 Objections to Dutch Book Arguments -- 9.3.1 The Package Principle -- 9.3.2 Dutch Strategy objections -- 9.4 Exercises -- 9.5 Further reading -- Notes -- 10: Accuracy Arguments -- 10.1 Accuracy as calibration -- 10.2 The gradational accuracy argument for probabilism -- 10.2.1 The Brier score.
10.2.2 Joyce's accuracy argument for probabilism -- 10.3 Objections to the accuracy argument for probabilism -- 10.3.1 The absolute-value score -- 10.3.2 Proper scoring rules -- 10.3.3 Are improper rules unacceptable? -- 10.4 Do we really need Finite Additivity? -- 10.5 An accuracy argument for Conditionalization -- 10.6 Exercises -- 10.7 Further reading -- Notes -- Further Reading -- PART V: CHALLENGES AND OBJECTIONS -- 11: Memory Loss and Self-locating Credences -- 11.1 Memory loss -- 11.1.1 The problem -- 11.1.2 A possible solution -- 11.1.3 Suppositional Consistency -- 11.2 Self-locating credences -- 11.2.1 The problem -- 11.2.2 The HTM approach -- 11.2.3 Going forward -- 11.3 Exercises -- 11.4 Further reading -- Notes -- 12: Old Evidence and Logical Omniscience -- 12.1 Old evidence -- 12.1.1 The problem -- 12.1.2 Solutions to the diachronic problem -- 12.1.3 Solutions to the synchronic problem -- 12.1.4 More radical solutions -- 12.2 Logical omniscience -- 12.2.1 Clutter avoidance and partial distributions -- 12.2.2 Logical confirmation and logical learning -- 12.2.3 Allowing logical uncertainty -- 12.2.4 Logical omniscience reconsidered -- 12.3 Exercises -- 12.4 Further reading -- Notes -- 13: The Problem of the Priors and Alternatives to Bayesianism -- 13.1 The Problem of the Priors -- 13.1.1 Understanding the problem -- 13.1.2 Washing out of priors -- 13.2 Frequentism -- 13.2.1 Significance testing -- 13.2.2 Troubles with significance testing -- 13.3 Likelihoodism -- 13.3.1 Troubles with likelihoodism -- 13.4 Exercises -- 13.5 Further reading -- Notes -- 14: Comparative Confidence, Ranged Credences, and Dempster-Shafer Theory -- 14.1 Comparative confidence -- 14.1.1 de Finetti's comparative conditions -- 14.1.2 The Scott Axiom -- 14.1.3 Extensions and challenges -- 14.2 Ranged credences -- 14.2.1 Ranged credences, representation, and evidence.
14.2.2 Extensions and challenges -- 14.3 Dempster-Shafer theory -- 14.4 Exercises -- 14.5 Further reading -- Notes -- Glossary for Volumes 1 &amp -- 2 -- Bibliography of Volumes 1 &amp -- 2 -- Index of Names in Volumes 1 &amp -- 2.
This edition also issued in print: 2022.
Includes bibliographical references and index.