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Inference, Method and Decision: Towards a Bayesian Philosophy of Science by R.D.
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- ISBN-13
- 9789027708175
- Book Title
- Inference, Method and Decision
- ISBN
- 9789027708175
- Subject Area
- Science
- Publication Name
- Inference, Method and Decision
- Publisher
- Springer Netherlands
- Item Length
- 9.3 in
- Subject
- Philosophy & Social Aspects, History
- Publication Year
- 1977
- Series
- Synthese Library
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Weight
- 44.4 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xv, 270 Pages
關於產品
Product Identifiers
Publisher
Springer Netherlands
ISBN-10
9027708177
ISBN-13
9789027708175
eBay Product ID (ePID)
4579379
Product Key Features
Number of Pages
Xv, 270 Pages
Publication Name
Inference, Method and Decision
Language
English
Publication Year
1977
Subject
Philosophy & Social Aspects, History
Type
Textbook
Subject Area
Science
Series
Synthese Library
Format
Hardcover
Dimensions
Item Weight
44.4 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
Series Volume Number
115
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
One: Informative Inference.- 1 / Information.- 2 / The Paradoxes of Confirmation.- 3 / Inductivism and Probabilism.- 4 / Inductive Generalization.- Two: Scientific Method.- 5 / Simplicity.- 6 / Bayes and Popper.- 7 / The Copernican Revelation.- 8 / Explanation.- Three: Statistical Decision.- 9 / Support.- 10 / Testing.- 11 / Bayes/Orthodox Comparisons.- 12 / Cognitive Decisions.- Author Index.
Synopsis
This book grew out of previously published papers of mine composed over a period of years; they have been reworked (sometimes beyond recognition) so as to form a reasonably coherent whole. Part One treats of informative inference. I argue (Chapter 2) that the traditional principle of induction in its clearest formulation (that laws are confirmed by their positive cases) is clearly false. Other formulations in terms of the 'uniformity of nature' or the 'resemblance of the future to the past' seem to me hopelessly unclear. From a Bayesian point of view, 'learning from experience' goes by conditionalization (Bayes' rule). The traditional stum- bling block for Bayesians has been to fmd objective probability inputs to conditionalize upon. Subjective Bayesians allow any probability inputs that do not violate the usual axioms of probability. Many subjectivists grant that this liberality seems prodigal but own themselves unable to think of additional constraints that might plausibly be imposed. To be sure, if we could agree on the correct probabilistic representation of 'ignorance' (or absence of pertinent data), then all probabilities obtained by applying Bayes' rule to an 'informationless' prior would be objective. But familiar contra- dictions, like the Bertrand paradox, are thought to vitiate all attempts to objectify 'ignorance'. BuUding on the earlier work of Sir Harold Jeffreys, E. T. Jaynes, and the more recent work ofG. E. P. Box and G. E. Tiao, I have elected to bite this bullet. In Chapter 3, I develop and defend an objectivist Bayesian approach., This book grew out of previously published papers of mine composed over a period of years; they have been reworked (sometimes beyond recognition) so as to form a reasonably coherent whole. Part One treats of informative inference. I argue (Chapter 2) that the traditional principle of induction in its clearest formulation (that laws are confirmed by their positive cases) is clearly false. Other formulations in terms of the 'uniformity of nature' or the 'resemblance of the future to the past' seem to me hopelessly unclear. From a Bayesian point of view, 'learning from experience' goes by conditionalization (Bayes' rule). The traditional stum bling block for Bayesians has been to fmd objective probability inputs to conditionalize upon. Subjective Bayesians allow any probability inputs that do not violate the usual axioms of probability. Many subjectivists grant that this liberality seems prodigal but own themselves unable to think of additional constraints that might plausibly be imposed. To be sure, if we could agree on the correct probabilistic representation of 'ignorance' (or absence of pertinent data), then all probabilities obtained by applying Bayes' rule to an 'informationless' prior would be objective. But familiar contra dictions, like the Bertrand paradox, are thought to vitiate all attempts to objectify 'ignorance'. BuUding on the earlier work of Sir Harold Jeffreys, E. T. Jaynes, and the more recent work ofG. E. P. Box and G. E. Tiao, I have elected to bite this bullet. In Chapter 3, I develop and defend an objectivist Bayesian approach.
LC Classification Number
Q174-175.3
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