Foundations bayesianism (54 résultats)
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Paperback. Etat : new. Paperback. Bayesianism is the view that rational degrees of belief should follow probability theory. In this book, philosopher Kenny Easwaran defends Bayesianism as an effective normative framework when applied to all "doxastic subjects": humans, animals, artificial intelligences, groups, and even hypothet…ical aliens or angels.The book addresses major challenges: concerns about numerical precision and computational tractability, problems with infinitesimals and regularity, difficulties with act-state dependence, and debates over evidence. Easwaran employs measurement theory to show probabilistic representation is analogous to representing temperature different scales (Fahrenheit/Celsius, probability/odds) can represent the same underlying non-numerical reality.Later chapters examine how beliefs should update through conditionalization, while distinguishing different sources of rational norms. Easwaran defends a notably permissive view: any probability function is internally rational, as reason alone cannot dictate privileged priors. However, "ecological rationality" explains why certain belief systems perform better in specific environments, acknowledging both Bayesianism's abstract permissiveness and practical constraints making certain credal states more successful. 1) Belief comes in degrees2) Belief guides action3) Belief aims at the truth4) The measurement of belief5) Zero isnt nothing6) Rational changes of belief7) The components of rationality8) Multiple rationalitiesConclusion Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Etat : New. Bayesianism is the view that rational degrees of belief should follow probability theory. In this book, philosopher Kenny Easwaran defends Bayesianism as an effective normative framework when applied to all "doxastic subjects": humans, animals, artificial intelligences, groups, and even hypothetical aliens… or angels.The book addresses major challenges: concerns about numerical precision and computational tractability, problems with infinitesimals and regularity, difficulties with act-state dependence, and debates over evidence. Easwaran employs measurement theory to show probabilistic representation is analogous to representing temperature - different scales (Fahrenheit/Celsius, probability/odds) can represent the same underlying non-numerical reality.Later chapters examine how beliefs should update through conditionalization, while distinguishing different sources of rational norms. Easwaran defends a notably permissive view: any probability function is internally rational, as reason alone cannot dictate privileged priors. However, "ecological rationality" explains why certain belief systems perform better in specific environments, acknowledging both Bayesianism's abstract permissiveness and practical constraints making certain credal states more successful.
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Paperback. Etat : New. Bayesianism is the view that rational degrees of belief should follow probability theory. In this book, philosopher Kenny Easwaran defends Bayesianism as an effective normative framework when applied to all "doxastic subjects": humans, animals, artificial intelligences, groups, and even hypothetical aliens… or angels.The book addresses major challenges: concerns about numerical precision and computational tractability, problems with infinitesimals and regularity, difficulties with act-state dependence, and debates over evidence. Easwaran employs measurement theory to show probabilistic representation is analogous to representing temperature - different scales (Fahrenheit/Celsius, probability/odds) can represent the same underlying non-numerical reality.Later chapters examine how beliefs should update through conditionalization, while distinguishing different sources of rational norms. Easwaran defends a notably permissive view: any probability function is internally rational, as reason alone cannot dictate privileged priors. However, "ecological rationality" explains why certain belief systems perform better in specific environments, acknowledging both Bayesianism's abstract permissiveness and practical constraints making certain credal states more successful.
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Hardback. Etat : New. 2002 ed. A collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. Some of these papers seek to clarify the relationships between Bayesian, causal and logical reasoning. Others consider the application of Bayesianism to artificial intelligence, decision… theory, statistics and the philosophy of science and mathematics. The volume includes important criticisms of Bayesian reasoning and also gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. The upshot is a plethora of new problems and directions for Bayesians to pursue. The book should be of interest to graduate students or researchers who wish to learn more about Bayesianism than can be provided by introductory textbooks to the subject. Those involved with the applications of Bayesian reasoning will find useful discussion on the validity of Bayesianism and its limits, while philosophers and others interested in pure reasoning should find ideas on normativity and the logic of belief.
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Etat : New. A collection of papers addressing the key challenges that face the Bayesian interpretation of probability. This work includes important criticisms of Bayesian reasoning and also gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. Editor(s): Corfield, David; Williamson,… Jon. Series: Applied Logic Series. Num Pages: 416 pages, 6 black & white illustrations, biography. BIC Classification: PBT; PDA. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 235 x 155 x 23. Weight in Grams: 785. . 2001. Hardback. . . . .
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Taschenbuch. Etat : Neu. Foundations of Bayesianism | D. Corfield (u. a.) | Taschenbuch | Applied Logic Series | xiii | Englisch | 2011 | Springer | EAN 9789048159208 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Hardback. Etat : New. Bayesianism is the view that rational degrees of belief should follow probability theory. In this book, philosopher Kenny Easwaran defends Bayesianism as an effective normative framework when applied to all "doxastic subjects": humans, animals, artificial intelligences, groups, and even hypothetical aliens…or angels.The book addresses major challenges: concerns about numerical precision and computational tractability, problems with infinitesimals and regularity, difficulties with act-state dependence, and debates over evidence. Easwaran employs measurement theory to show probabilistic representation is analogous to representing temperature - different scales (Fahrenheit/Celsius, probability/odds) can represent the same underlying non-numerical reality.Later chapters examine how beliefs should update through conditionalization, while distinguishing different sources of rational norms. Easwaran defends a notably permissive view: any probability function is internally rational, as reason alone cannot dictate privileged priors. However, "ecological rationality" explains why certain belief systems perform better in specific environments, acknowledging both Bayesianism's abstract permissiveness and practical constraints making certain credal states more successful.
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. Some of these papers seek to clarify the relationships between Bayesian, causal and logical re…asoning. Others consider the application of Bayesianism to artificial intelligence, decision theory, statistics and the philosophy of science and mathematics. The volume includes important criticisms of Bayesian reasoning and also gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. The upshot is a plethora of new problems and directions for Bayesians to pursue.The book will be of interest to graduate students or researchers who wish to learn more about Bayesianism than can be provided by introductory textbooks to the subject. Those involved with the applications of Bayesian reasoning will find essential discussion on the validity of Bayesianism and its limits, while philosophers and others interested in pure reasoning will find new ideas on normativity and the logic of belief.
