Philosophy of Probability

Specificaties
Paperback, 296 blz. | Engels
Springer Netherlands | 0e druk, 2010
ISBN13: 9789048143016
Rubricering
Springer Netherlands 0e druk, 2010 9789048143016
Onderdeel van serie Philosophical Studies Series
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

Philosophy of Probability provides a comprehensive introduction to theoretical issues that occupy a central position in disciplines ranging from philosophy of mind and epistemology to cognitive science, decision theory and artificial intelligence. Some contributions shed new light on the standard conceptions of probability (Bayesianism, logical and computational theories); others offer detailed analyses of two important topics in the field of cognitive science: the meaning and the representation of (partial) belief, and the management of uncertainty. The authors of this well-balanced account are philosophers as well as computer scientists (among them, L.J. Cohen, D. Miller, P. Gärdenfors, J. Vickers, D. Dubois and H. Prade). This multidisciplinary approach to probability is designed to illuminate the intricacies of the problems in the domain of cognitive inquiry. No one interested in epistemology or aritificial intelligence will want to miss it.

Specificaties

ISBN13:9789048143016
Taal:Engels
Bindwijze:paperback
Aantal pagina's:296
Uitgever:Springer Netherlands
Druk:0

Inhoudsopgave

Introduction. 1: Personalistic Bayesianism; C. Howson. 2: On Higher Order Beliefs; N.-E. Sahlin. 3: On the Logic of Relevance; P. Gärdenfors. 4: Diverging Distributions; D. Miller. 5: Inductive Logic Revisited; J.-P. Dubucs. 6: Probability and Utility; J.M. Vickers. 7: What has Probability to Do with Strength of Belief; L.J. Cohen. 8: Randomness, Unpredictability and Absence of Order: the Identification by the Theory of Recursivity of the Mathematical Notion of Random Sequence; J.-P. Delahaye. 9: A Glance at Non-Standard Models and Logics of Uncertainty and Vaguness; D. Dubois, H. Prade. 10: Causal Laws are Objectifications of Inductive Schemes; W. Spohn. 11. Probabilistic Inference in Artificial Intelligence: the Method of Bayesian networks; J.-L. Golmard.

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