Description

Book Synopsis
The authors believe that a proper treatment of probability theory requires an adequate background in the theory of finite measures in general spaces. The first part of their book sets out this material in a form which not only provides an introduction for intending specialists in measure theory but also meets the needs of students of probability.

Table of Contents
Preface; 1. Theory of sets; 2. Point set topology; 3. Set functions; 4. Construction and propertied of measures; 5. Definitions and properties of the integral; 6. Related spaces and measures; 7. The space of measurable functions; 8. Linear functionals; 9. Structure of measures in special spaces; 10. What is probability?; 11. Random variables; 12. Characteristic functions; 13. Independence; 14. Finite collections of random variables; 15. Stochastic processes.

Introdction to Measure and Probability

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    A Paperback by J. F. C. Kingman, S. J. Taylor

    15 in stock


      View other formats and editions of Introdction to Measure and Probability by J. F. C. Kingman

      Publisher: Cambridge University Press
      Publication Date: 11/20/2008 12:00:00 AM
      ISBN13: 9780521090322, 978-0521090322
      ISBN10: 0521090326
      Also in:
      Probability and statistics

      Description

      Book Synopsis
      The authors believe that a proper treatment of probability theory requires an adequate background in the theory of finite measures in general spaces. The first part of their book sets out this material in a form which not only provides an introduction for intending specialists in measure theory but also meets the needs of students of probability.

      Table of Contents
      Preface; 1. Theory of sets; 2. Point set topology; 3. Set functions; 4. Construction and propertied of measures; 5. Definitions and properties of the integral; 6. Related spaces and measures; 7. The space of measurable functions; 8. Linear functionals; 9. Structure of measures in special spaces; 10. What is probability?; 11. Random variables; 12. Characteristic functions; 13. Independence; 14. Finite collections of random variables; 15. Stochastic processes.

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