The Probabilistic Foundations of Rational Learning

Nonfiction, Religion & Spirituality, Philosophy, Logic, Epistemology
Cover of the book The Probabilistic Foundations of Rational Learning by Simon M. Huttegger, Cambridge University Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Simon M. Huttegger ISBN: 9781108506175
Publisher: Cambridge University Press Publication: October 19, 2017
Imprint: Cambridge University Press Language: English
Author: Simon M. Huttegger
ISBN: 9781108506175
Publisher: Cambridge University Press
Publication: October 19, 2017
Imprint: Cambridge University Press
Language: English

According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sciences.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sciences.

More books from Cambridge University Press

Cover of the book Theodosius II by Simon M. Huttegger
Cover of the book The Cambridge Companion to World Literature by Simon M. Huttegger
Cover of the book Antifascist Humanism and the Politics of Cultural Renewal in Germany by Simon M. Huttegger
Cover of the book Schoenberg and Hollywood Modernism by Simon M. Huttegger
Cover of the book The Learning Sciences in Educational Assessment by Simon M. Huttegger
Cover of the book The Cambridge Companion to Henry James by Simon M. Huttegger
Cover of the book Political Philosophy versus History? by Simon M. Huttegger
Cover of the book Losing the Temple and Recovering the Future by Simon M. Huttegger
Cover of the book Regional Trade Agreements and the Multilateral Trading System by Simon M. Huttegger
Cover of the book Defending Biodiversity by Simon M. Huttegger
Cover of the book Evidence and Faith by Simon M. Huttegger
Cover of the book The Child in International Refugee Law by Simon M. Huttegger
Cover of the book Feminist Judgments by Simon M. Huttegger
Cover of the book Race, Nation, and Citizenship in Postcolonial Africa by Simon M. Huttegger
Cover of the book The Political Economy of Human Happiness by Simon M. Huttegger
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy