Fundamentals of Bayesian Epistemology 1 PDF Download
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Author: Michael G. Titelbaum
Publisher: Oxford University Press
ISBN: 0198707606
Category : Bayesian statistical decision theory
Languages : en
Pages : 217
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Book Description
'Fundamentals of Bayesian Epistemology' provides an accessible introduction to the key concepts and principles of the Bayesian formalism. This volume introduces degrees of belief as a concept in epistemology and the rules for updating degrees of belief derived from Bayesian principles.--
Author: Michael G. Titelbaum
Publisher: Oxford University Press
ISBN: 0198707606
Category : Bayesian statistical decision theory
Languages : en
Pages : 217
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Book Description
'Fundamentals of Bayesian Epistemology' provides an accessible introduction to the key concepts and principles of the Bayesian formalism. This volume introduces degrees of belief as a concept in epistemology and the rules for updating degrees of belief derived from Bayesian principles.--
Author: Michael G. Titelbaum
Publisher: Oxford University Press
ISBN: 0192863142
Category : Bayesian statistical decision theory
Languages : en
Pages : 416
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Book Description
'Fundamentals of Bayesian Epistemology' provides an accessible introduction to the key concepts and principles of the Bayesian formalism. Volume 2 introduces applications of Bayesianism to confirmation and decision theory, then gives a critical survey of arguments for and challenges to Bayesian epistemology.--
Author: Luc Bovens
Publisher: OUP Oxford
ISBN: 0191533521
Category : Philosophy
Languages : en
Pages : 170
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Book Description
Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information sources. Measurement of the coherence of information is a controversial matter: arguably, the more coherent a set of information is, the more confident we may be that its content is true, other things being equal. The authors offer a new treatment of coherence which respects this claim and shows its relevance to scientific theory choice. Bovens and Hartmann apply this methodology to a wide range of much discussed issues regarding evidence, testimony, scientific theories, and voting. Bayesian Epistemology is an essential tool for anyone working on probabilistic methods in philosophy, and has broad implications for many other disciplines.
Author: Jan Sprenger
Publisher: Oxford University Press
ISBN: 0191652229
Category : Philosophy
Languages : en
Pages : 414
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Book Description
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.
Author: Jean-Michel Marin
Publisher: Springer Science & Business Media
ISBN: 0387389792
Category : Computers
Languages : en
Pages : 265
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Book Description
This Bayesian modeling book provides the perfect entry for gaining a practical understanding of Bayesian methodology. It focuses on standard statistical models and is backed up by discussed real datasets available from the book website.
Author: Michael G. Titelbaum
Publisher: Oxford University Press
ISBN: 0199658307
Category : Mathematics
Languages : en
Pages : 362
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Book Description
This book presents a new Bayesian framework for modeling rational degrees of belief, called the Certainty-Loss Framework.
Author: Michael G. Titelbaum
Publisher:
ISBN: 9780191954108
Category : Bayesian statistical decision theory
Languages : en
Pages : 192
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Book Description
'Fundamentals of Bayesian Epistemology' provides an accessible introduction to the key concepts and principles of the Bayesian formalism. This volume introduces degrees of belief as a concept in epistemology and the rules for updating degrees of belief derived from Bayesian principles.
Author: Franz Huber
Publisher: Springer Science & Business Media
ISBN: 1402091982
Category : Philosophy
Languages : en
Pages : 352
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Book Description
This anthology is the first book to give a balanced overview of the competing theories of degrees of belief. It also explicitly relates these debates to more traditional concerns of the philosophy of language and mind and epistemic logic.
Author: James M. Joyce
Publisher: Cambridge University Press
ISBN: 9780521641647
Category : Computers
Languages : en
Pages : 300
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Book Description
The book also contains a major new discussion of what it means to suppose that some event occurs or that some proposition is true.
Author: Mark Kaplan
Publisher: Cambridge University Press
ISBN: 9780521624961
Category : Philosophy
Languages : en
Pages : 250
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Book Description
Kaplan presents an accessible new variant on Bayesian decision theory.
