Convergence of Probability Measures, Second Edition
β Scribed by Patrick Billingsley(auth.)
- Year
- 1999
- Tongue
- English
- Leaves
- 286
- Series
- Wiley Series in Probability and Statistics
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
A new look at weak-convergence methods in metric spaces-from a master of probability theory In this new edition, Patrick Billingsley updates his classic work Convergence of Probability Measures to reflect developments of the past thirty years. Widely known for his straightforward approach and reader-friendly style, Dr. Billingsley presents a clear, precise, up-to-date account of probability limit theory in metric spaces. He incorporates many examples and applications that illustrate the power and utility of this theory in a range of disciplines-from analysis and number theory to statistics, engineering, economics, and population biology. With an emphasis on the simplicity of the mathematics and smooth transitions between topics, the Second Edition boasts major revisions of the sections on dependent random variables as well as new sections on relative measure, on lacunary trigonometric series, and on the Poisson-Dirichlet distribution as a description of the long cycles in permutations and the large divisors of integers. Assuming only standard measure-theoretic probability and metric-space topology, Convergence of Probability Measures provides statisticians and mathematicians with basic tools of probability theory as well as a springboard to the "industrial-strength" literature available today.Content:
Chapter 1 Weak Convergence in Metric Spaces (pages 7β79):
Chapter 2 The Space C (pages 80β120):
Chapter 3 The Space D (pages 121β179):
Chapter 4 Dependent Variables (pages 180β206):
Chapter 5 Other Modes of Convergence (pages 207β235):
π SIMILAR VOLUMES
Probability and Measure Theory, Second Edition, is a text for a graduate-level course in probability that includes essential background topics in analysis. It provides extensive coverage of conditional probability and expectation, strong laws of large numbers, martingale theory, the central limit th