Foundations of Probability with Applications Selected Papers 1974}}}1995 [Hardcover]

This is an important collection of essays by a leading philosopher, dealing with the foundations of probability.Part I comprises five essays on the axiomatic foundations of probability. Part II contains seven articles on probabilistic causality and quantum mechanics. The third part consists of a single extended essay applying probabilistic theories of learning to practical questions of education.Part I comprises five essays on the axiomatic foundations of probability. Part II contains seven articles on probabilistic causality and quantum mechanics. The third part consists of a single extended essay applying probabilistic theories of learning to practical questions of education.This is an important collection of essays on dealing with the foundations of probability that will be of value to philosophers of science, mathematicians, statisticians, psychologists and educationalists. The collection falls into three parts: Part I comprises five essays on the axiomatic foundations of probability. Part II contains seven articles on probabilistic causality and quantum mechanics, with an emphasis on the existence of hidden variables. The third part consists of a single extended essay applying probabilistic theories of learning to practical questions of education: it incorporates extensive data analysis.Part I. Foundations of Probability: 1. Necessary and sufficient conditions for existence of a unique measure strictly agreeing with a qualititative probability ordering; 2. Necessary and sufficient qualitative axioms for conditional probability; 3. On using random relations to generate upper and lower probabilities; 4. Conditions on upper and lower probabilities to imply probabilities; 5. Qualitative axioms for random-variable representation of extensive quantifiers; Part II. Causality and Quantum Mechanics: 6. Stochastic incompleteness of quantum mechanics; 7. On the determinism of hidden variable theories with strict correlation and conditional statistical independence oflœ