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Philosophy and probability /

Probability is increasingly important for our understanding of the world. What is probability? How do we model it, and how do we use it? Timothy Childers presents a lively introduction to the foundations of probability and to philosophical issues it raises. He keeps technicalities to a minimum, and...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Childers, Timothy (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Oxford : Oxford University Press, [2013]
Temas:
Acceso en línea:Texto completo

MARC

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520 |a Probability is increasingly important for our understanding of the world. What is probability? How do we model it, and how do we use it? Timothy Childers presents a lively introduction to the foundations of probability and to philosophical issues it raises. He keeps technicalities to a minimum, and assumes no prior knowledge of the subject. He explains the main interpretations of probability-frequentist, propensity, classical, Bayesian, and objective Bayesian-and uses stimulatingexamples to bring the subject to life. All students of philosophy will benefit from an understanding of probability, 
504 |a Includes bibliographical references (pages 182-192) and index. 
505 0 |a Probabilities and relative frequencies -- Propensities and other physical probabilities -- Subjective probabilities -- Subjective and objective probabilities -- The classical and logical interpretations -- The maximum entropy principle. 
588 0 |a Print version record. 
546 |a English. 
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650 7 |a MATHEMATICS  |x Probability & Statistics  |x General.  |2 bisacsh 
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880 8 |6 505-00/(S  |a 6.2.3 Determination of continuous probabilities -- 6.3 Maximum Entropy and the Wine-Water Paradox -- 6.3.1 Problems with the solution-dimensions or not-- 6.4 Language Dependence -- 6.4.1 The statistical mechanics counterexample -- 6.4.2 Correctly applying the Principle-- 6.4.3 Language dependence and ontological dependence -- 6.4.4 The scope of the Maximum Entropy Principle -- 6.5 Justifying the Maximum Entropy Principle as a Logical Constraint -- 6.5.1 Maximum entropy as imposing consistency -- 6.5.2 Problems with the Maximum Entropy Principle as consistency -- 6.6 Conclusion -- Appendices -- A.0 Some Basics -- A.0.1 Percentages -- A.0.2 Kinds of numbers -- A.0.3 Sizes of sets-countable and uncountable -- A.0.4 Functions, limits -- A.0.5 Logarithms -- A.1 The Axioms -- A.1.1 Conditional probability, independence -- A.2 Measures, Probability Measures -- A.2.1 Fields -- A.2.2 Fields, σ-fields -- A.2.3 Measures -- A.2.4 Probability measures -- A.2.5 Some useful theorems -- A.3 Random Variables -- A.3.1 Sums of random variables -- A.3.2 Expectation -- A.3.3 Continuous random variables -- A.4 Combinatorics -- A.4.1 Permutations -- A.4.2 Combinations -- A.5 Laws of Large Numbers -- A.5.1 Bernoulli random variables and the binomial distribution -- A.5.2 Laws of Large Numbers -- A.5.3 Behaviour of the binomial distribution for large numbers of trials -- A.6 Topics in Subjective Probability -- A.6.1 Strict coherence -- A.6.2 Scoring rules -- A.6.3 Axioms for qualitative and quantitative probabilities -- A.7 The Duhem-Quine Problem, Language, Metaphysics -- A.7.1 A probabilistic translation of Quine's programme -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Z. 
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