Bayesianism And Scientific Reasoning
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Bayesianism And Scientific Reasoning
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Author : Jonah N. Schupbach
language : en
Publisher: Cambridge University Press
Release Date : 2022-01-31
Bayesianism And Scientific Reasoning written by Jonah N. Schupbach and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-31 with Science categories.
This Element explores the Bayesian approach to the logic and epistemology of scientific reasoning. Section 1 introduces the probability calculus as an appealing generalization of classical logic for uncertain reasoning. Section 2 explores some of the vast terrain of Bayesian epistemology. Three epistemological postulates suggested by Thomas Bayes in his seminal work guide the exploration. This section discusses modern developments and defenses of these postulates as well as some important criticisms and complications that lie in wait for the Bayesian epistemologist. Section 3 applies the formal tools and principles of the first two sections to a handful of topics in the epistemology of scientific reasoning: confirmation, explanatory reasoning, evidential diversity and robustness analysis, hypothesis competition, and Ockham's Razor.
Scientific Reasoning
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Author : Colin Howson
language : en
Publisher:
Release Date : 1993
Scientific Reasoning written by Colin Howson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Mathematics categories.
"Scientific Reasoning: The Bayesian Approach explains, in an accessible style, those elements of the probability calculus that are relevant to Bayesian methods, and argues that the probability calculus is best regarded as a species of logic." "Howson and Urbach contrast the Bayesian with the 'classical' view that was so influential in the last century, and demonstrate that familiar classical procedures for evaluating statistical hypotheses, such as significance tests, point estimation, confidence intervals, and other techniques, provide an utterly false basis for scientific inference. They also expose the well-known non-probabilistic philosophies of Popper, Lakatos, and Kuhn as similarly unscientific." "Scientific Reasoning shows how Bayesian theory, by contrast with these increasingly discredited approaches, provides a unified and highly satisfactory account of scientific method, an account which practicing scientists and all those interested in the sciences ought to master."--BOOK JACKET.
Bayesian Philosophy Of Science
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Author : Jan Sprenger
language : en
Publisher: Oxford University Press
Release Date : 2019-08-23
Bayesian Philosophy Of Science written by Jan Sprenger and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-23 with Philosophy categories.
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.
Bayesian Reasoning In Data Analysis
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Author : Giulio D'Agostini
language : en
Publisher: World Scientific
Release Date : 2003
Bayesian Reasoning In Data Analysis written by Giulio D'Agostini and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Mathematics categories.
A multi-level introduction to Bayesian reasoning. The basic ideas of this approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; comparison of hypotheses; and more.
Bayesian Rationality
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Author : Mike Oaksford
language : en
Publisher: Oxford University Press
Release Date : 2007-02-22
Bayesian Rationality written by Mike Oaksford and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-02-22 with Philosophy categories.
For almost 2,500 years, the Western concept of what is to be human has been dominated by the idea that the mind is the seat of reason - humans are, almost by definition, the rational animal. In this text a more radical suggestion for explaining these puzzling aspects of human reasoning is put forward.
Foundations Of Bayesianism
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Author : D. Corfield
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Foundations Of Bayesianism written by D. Corfield and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Science categories.
This is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. The volume includes important criticisms of Bayesian reasoning and gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. It will be of interest to graduate students, researchers, those involved with the applications of Bayesian reasoning, and philosophers.
Probability And Evidence
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Author : Paul Horwich
language : en
Publisher: Cambridge University Press
Release Date : 2016-08-26
Probability And Evidence written by Paul Horwich and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-26 with Philosophy categories.
This influential book offers a probabilistic approach to scientific reasoning to resolve central issues in the philosophy of science.
Bayesian Inference And Maximum Entropy Methods In Science And Engineering
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Author : Rainer Fischer
language : en
Publisher: A I P Press
Release Date : 2004-11-19
Bayesian Inference And Maximum Entropy Methods In Science And Engineering written by Rainer Fischer and has been published by A I P Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-11-19 with Mathematics categories.
All papers were peer reviewed. Bayesian Inference and Maximum Entropy Methods in Science and Engineering provide a framework for analyzing ill-conditioned data. Maximum Entropy is a theoretical method to draw conclusions when little information is available. Bayesian probability theory provides a formalism for scientific reasoning by analyzing noisy or imcomplete data using prior knowledge.
The Probabilistic Mind
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Author : Nick Chater
language : en
Publisher: OUP Oxford
Release Date : 2008
The Probabilistic Mind written by Nick Chater and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Philosophy categories.
The Probabilistic Mind is a follow-up to the influential and highly cited Rational Models of Cognition (OUP, 1998). It brings together developmetns in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods.
Improving Bayesian Reasoning What Works And Why
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Author : Gorka Navarrete
language : en
Publisher: Frontiers Media SA
Release Date : 2016-02-02
Improving Bayesian Reasoning What Works And Why written by Gorka Navarrete and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-02 with Electronic book categories.
We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a normative approach to probabilistic belief revision and, as such, it is in need of no improvement. Rather, it is the typical individual whose reasoning and judgments often fall short of the Bayesian ideal who is the focus of improvement. What have we learnt from over a half-century of research and theory on this topic that could explain why people are often non-Bayesian? Can Bayesian reasoning be facilitated, and if so why? These are the questions that motivate this Frontiers in Psychology Research Topic. Bayes' theorem, named after English statistician, philosopher, and Presbyterian minister, Thomas Bayes, offers a method for updating one’s prior probability of an hypothesis H on the basis of new data D such that P(H|D) = P(D|H)P(H)/P(D). The first wave of psychological research, pioneered by Ward Edwards, revealed that people were overly conservative in updating their posterior probabilities (i.e., P(D|H)). A second wave, spearheaded by Daniel Kahneman and Amos Tversky, showed that people often ignored prior probabilities or base rates, where the priors had a frequentist interpretation, and hence were not Bayesians at all. In the 1990s, a third wave of research spurred by Leda Cosmides and John Tooby and by Gerd Gigerenzer and Ulrich Hoffrage showed that people can reason more like a Bayesian if only the information provided takes the form of (non-relativized) natural frequencies. Although Kahneman and Tversky had already noted the advantages of frequency representations, it was the third wave scholars who pushed the prescriptive agenda, arguing that there are feasible and effective methods for improving belief revision. Most scholars now agree that natural frequency representations do facilitate Bayesian reasoning. However, they do not agree on why this is so. The original third wave scholars favor an evolutionary account that posits human brain adaptation to natural frequency processing. But almost as soon as this view was proposed, other scholars challenged it, arguing that such evolutionary assumptions were not needed. The dominant opposing view has been that the benefit of natural frequencies is mainly due to the fact that such representations make the nested set relations perfectly transparent. Thus, people can more easily see what information they need to focus on and how to simply combine it. This Research Topic aims to take stock of where we are at present. Are we in a proto-fourth wave? If so, does it offer a synthesis of recent theoretical disagreements? The second part of the title orients the reader to the two main subtopics: what works and why? In terms of the first subtopic, we seek contributions that advance understanding of how to improve people’s abilities to revise their beliefs and to integrate probabilistic information effectively. The second subtopic centers on explaining why methods that improve non-Bayesian reasoning work as well as they do. In addressing that issue, we welcome both critical analyses of existing theories as well as fresh perspectives. For both subtopics, we welcome the full range of manuscript types.