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Author Billingsley, Patrick.

Title Probability and measure / Patrick Billingsley.

Published New York : J. Wiley & Sons, [1995]


Location Call No. Status
 UniM Store ERC  519.2 BILL  3rd ed.    DUE 26-05-21
Edition 3rd ed.
Physical description xii, 593 pages : illustrations ; 25 cm.
Series Wiley series in probability and mathematical statistics.
Wiley series in probability and mathematical statistics.
Notes "A Wiley-Interscience publication."
Bibliography Includes bibliographical references (pages 581-583) and index.
Contents Ch. 1. Probability. 1. Borel's Normal Number Theorem. 2. Probability Measures. 3. Existence and Extension. 4. Denumerable Probabilities. 5. Simple Random Variables. 6. The Law of Large Numbers. 7. Gambling Systems. 8. Markov Chains. 9. Large Deviations and the Law of the Iterated Logarithm -- Ch. 2. Measure. 10. General Measures. 11. Outer Measure. 12. Measures in Euclidean Space. 13. Measurable Functions and Mappings. 14. Distribution Functions -- Ch. 3. Integration. 15. The Integral. 16. Properties of the Integral. 17. The Integral with Respect to Lebesgue Measure. 18. Product Measure and Fubini's Theorem. 19. The L[superscript p] Spaces -- Ch. 4. Random Variables and Expected Values. 20. Random Variables and Distributions. 21. Expected Values. 22. Sums of Independent Random Variables. 23. The Poisson Process. 24. The Ergodic Theorem -- Ch. 5. Convergence of Distributions. 25. Weak Convergence. 26. Characteristic Functions. 27. The Central Limit Theorem.
28. Infinitely Divisible Distributions. 29. Limit Theorems in R[superscript k]. 30. The Method of Moments -- Ch. 6. Derivatives and Conditional Probability. 31. Derivatives on the Line. 32. The Radon-Nikodym Theorem. 33. Conditional Probability. 34. Conditional Expectation. 35. Martingales -- Ch. 7. Stochastic Processes. 36. Kolmogorov's Existence Theorem. 37. Brownian Motion. 38. Nondenumerable Probabilities.
Summary Like the previous editions, this new edition will be well received by students of mathematics, statistics, economics, and a wide variety of disciplines that require a solid understanding of probability theory.
Subject Probabilities.
Measure theory.
ISBN 0471007102 (acid-free paper)