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E-grāmata: Bayesian Philosophy of Science

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(LMU MunichLMU Munich), (University of Turin)
  • Formāts: 384 pages
  • Izdošanas datums: 23-Aug-2019
  • Izdevniecība: Oxford University Press
  • Valoda: eng
  • ISBN-13: 9780191652226
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Bayesian Philosophy of SciencePalielināt
  • Formāts: 384 pages
  • Izdošanas datums: 23-Aug-2019
  • Izdevniecība: Oxford University Press
  • Valoda: eng
  • ISBN-13: 9780191652226
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How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms of a cycle of variations on the theme of representing rational degrees of belief by means of subjective probabilities (and changing them by Bayesian conditionalization). In doing so, they integrate Bayesian inference--the leading theory of rationality in social science--with the practice of 21st century science. Bayesian Philosophy of Science thereby shows how modeling such attitudes improves our understanding of causes, explanations, confirming evidence, and scientific models in general. It combines a scientifically minded and mathematically sophisticated approach with conceptual analysis and attention to methodological problems of modern science, especially in statistical inference, and is therefore a valuable resource for philosophers and scientific practitioners.

Recenzijas

For anyone with a serious interest in formal methods in the philosophy of science, this book is essential reading. * James Wilson, Metascience * Bayesian Philosophy of Science gradually raises and broaches important and fascinating questions concerning the status, foundations, and limits of Bayesianism and Bayesian inference. Reading the entirety of this rich and stimulating book, which is both accomplished and forward-looking, is therefore rewarding and highly recommended. * Isabelle Drouet, OEconomia * Sprenger and Hartmann's Bayesian Philosophy of Science promises to become the new reference manual for all things Bayesian in the philosophy of science...For anyone with a serious interest in formal methods in the philosophy of science, this book is essential reading. * James Wilson, Metascience * Detractors will read Sprenger and Hartmann's (hereafter S & H) Bayesian Philosophy of Science, because to my knowledge there is no other book that so effectively demonstrates the power and versatility of the Bayesian approach...The authors manage to cover a great deal of material, including material not typically discussed in introductions to Bayesian philosophy, such as the minimum divergence approach to probabilistic updating. * Olav Benjamin Vassend, Erkenntnis *

First Author's Preface vii
Second Author's Preface xiii
List of Figures
xxi
List of Tables
xxiii
Book Overview xxv
Theme: Bayesian Philosophy of Science 1(40)
Probability and Degrees of Belief
4(13)
Conditional Degrees of Belief and Bayes' Theorem
17(4)
Conditionalization and Varieties of Bayesian Inference
21(10)
Causal Bayesian Networks
31(4)
Variations on a Bayesian Theme
35(6)
Variation 1 Confirmation and Induction
41(26)
1.1 Motivating Bayesian Confirmation Theory
42(1)
1.2 Confirmation as Firmness
43(7)
1.3 Confirmation as Increase in Firmness and the Paradoxes of Confirmation
50(5)
1.4 The Plurality of Bayesian Confirmation Measures
55(6)
1.5 Discussion
61(6)
Appendix: Proofs of the Theorems
63(4)
Variation 2 The No Alternatives Argument
67(14)
2.1 Modeling the No Alternatives Argument
68(6)
2.2 Results
74(1)
2.3 Discussion
75(6)
Appendix: Proofs of the Theorems
78(3)
Variation 3 Scientific Realism and the No Miracles Argument
81(26)
3.1 The Bayesian No Miracles Argument
82(6)
3.2 Extending the No Miracles Argument to Stable Scientific Theories
88(7)
3.3 The Frequency-Based No Miracles Argument
95(4)
3.4 Discussion
99(8)
Appendix: Proofs of the Theorems
102(5)
Variation 4 Learning Conditional Evidence
107(24)
4.1 Divergence Minimization and Bayesian Conditionalization
110(3)
4.2 Three Challenges for Minimizing Divergence
113(2)
4.3 Meeting the Challenges
115(6)
4.4 Learning Relative Frequencies: The Case of Judy Benjamini
121(2)
4.5 Discussion
123(8)
Appendix: Proofs of the Theorems
126(5)
Variation 5 The Problem of Old Evidence
131(24)
5.1 The Dynamic Problem of Old Evidence: The Garber-Jeffrey-Niiniluoto Approach
133(5)
5.2 The Dynamic Problem of Old Evidence: Alternative Explanations
138(2)
5.3 The Static Problem of Old Evidence: A Counterfactual Perspective
140(3)
5.4 The Hybrid Problem of Old Evidence: Learning Explanatory Relationships
143(4)
5.5 Discussion
147(8)
Appendix: Proofs of the Theorems
150(5)
Variation 6 Causal Strength
155(30)
6.1 Interventions and Causal Bayesian Networks
156(5)
6.2 Probabilistic Measures of Causal Strength
161(10)
6.3 Causal Contribution and Actual Causal Strength
171(5)
6.4 Conclusion
176(9)
Appendix: Proofs of the Theorems
178(7)
Variation 7 Explanatory Power
185(22)
7.1 Causal Theories of Explanatory Power
187(3)
7.2 Statistical Relevance and Explanatory Power
190(2)
7.3 Representation Theorems for Measures of Explanatory Power
192(7)
7.4 Comparison of the Measures
199(2)
7.5 Discussion
201(6)
Appendix: Proofs of the Theorems
204(3)
Variation 8 Intertheoretic Reduction
207(20)
8.1 The Generalized Nagel-Schaffner Model
208(3)
8.2 Reduction and Confirmation
211(6)
8.3 Why Accept a Purported Reduction?
217(2)
8.4 Discussion
219(8)
Appendix: Proofs of the Theorems
222(5)
Variation 9 Hypothesis Tests and Corroboration
227(34)
9.1 Confirmation versus Corroboration
232(3)
9.2 Popper on Degree of Corroboration
235(3)
9.3 The Impossibility Results
238(7)
9.4 A New Explication of Corroboration
245(6)
9.5 Discussion
251(10)
Appendix: Proofs of the Theorems
254(7)
Variation 10 Simplicity and Model Selection
261(26)
10.1 Simplicity in Model Selection
263(4)
10.2 The Akaike Information Criterion
267(3)
10.3 The Bayesian Information Criterion
270(3)
10.4 The Minimum Message Length Principle
273(4)
10.5 The Deviance Information Criterion
277(3)
10.6 Discussion
280(7)
Appendix: Sketch of the Derivation of the Akaike Information Criterion
284(3)
Variation 11 Scientific Objectivity
287(24)
11.1 The Objections
289(2)
11.2 Convergence Theorems and Bayes Factors
291(2)
11.3 Frequentism and Scientific Objectivity
293(5)
11.4 Beyond Concordant, Value-Free and Procedural Objectivity
298(2)
11.5 Interactive and Convergent Objectivity
300(7)
11.6 Discussion
307(4)
Variation 12 Models, Idealizations and Objective Chance
311(16)
12.1 The Equality and Chance-Credence Coordination...
313(3)
12.2 The Suppositional Analysis
316(3)
12.3 Suppositional Prior Probabilities and the Trilemma Resolution
319(3)
12.4 Bayes' Theorem Revisited
322(2)
12.5 Conclusion
324(3)
Conclusion: The Theme Revisited 327(12)
Bibliography 339(38)
Index 377
Jan Sprenger is Professor of Philosophy of Science at the University of Turin. After completing an undergraduate degree in mathematics, he obtained his PhD in Philosophy at the University of Bonn in 2008. He then took up a post at Tilburg University, first working as Assistant Professor (2008-13) and subsequently as Full Professor (2014-17). He also directed the Tilburg Center for Logic, Ethics and Philosophy of Science (TiLPS). Sprenger's research and publications span a wide range of topics, mainly in philosophy of science and uncertain reasoning, but also in logic, group decision-making, and empirical work on human cognition.

Stephan Hartmann is Professor of Philosophy of Science at LMU Munich, Alexander von Humboldt Professor, and Co-Director of the Munich Center for Mathematical Philosophy (MCMP). Between 2007 and 2012 he worked at Tilburg University, where he was Chair in Epistemology and Philosophy of Science and Director of the Tilburg Center for Logic and Philosophy of Science (TiLPS). Prior to this, he was Professor of Philosophy at the London School of Economics and Director of its Centre for Philosophy of Natural and Social Science. He was President of the European Philosophy of Science Association (2013-17) and President of the European Society for Analytic Philosophy (2014-17). Hartmann's primary research and teaching areas are philosophy of science, philosophy of physics, formal epistemology, and social epistemology. His current interests also include the philosophy and psychology of reasoning and argumentation.
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