Weak Convergence of Measures

PrefaceChapter I Spaces, Mappings, and Measures 1. Classes of Sets 2. Alexandrov Spaces, Topological Spaces, and Measurable Spaces 3. Mappings 4. Classes of Bounded, Real-Valued, Continuous Functions and Measurable Functions 5. Normal Spaces and Completely Normal Spaces 6. Sequences of Sets 7. Metric Spaces 8. Mappings into Metric Spaces 9. Product Spaces 10. Product Spaces of Infinitely Many Factors 11. Some Particular Metric Spaces 12. Measures on an Algebra of Subsets 13. Measures on A-Spaces 14. Extensions of Measures 15. Measures on Infinite-Dimensional Product Spaces 16. Completion of Measures, Continuity Almost Surely and Almost EverywhereChapter II Integrals, Bounded, Linear Functionals, and Measures 1. Integrals as Nonnegative, Bounded, Linear Functionals 2. Generalizations of the Abstract Integral 3. The Representations of Bounded, Linear Functionals by Integrals 4. Measures Belonging to a Nonnegative, Bounded, Linear Functional on a Normal A-Space 5. Transformations of Measures and Integrals 6. Constructions of Measures on Metric Spaces by Riemann-Stieltjes Integrals 7. Measures on Product Spaces 8. Convolutions of Measures 9. Probability Spaces and Random Variables 10. Expectations, Conditional Expectations, and Conditional Probabilities 11. The Jensen InequalityChapter III Weak Convergence in Normal Spaces 1. Weak Convergence of Sequences of Measures on Normal Spaces 2. Weak Convergence of Sequences of Induced Measures and Transformed Measures 3. Uniformly s-Smooth Sequences of Measures 4. Weak Limits of s-Smooth Measures on Completely Normal A-Spaces 5. Reduction of Weak Limit Problems by Transformations 6. The Reduction Procedure for Metric Spaces 7. Weak Convergence of Tight Sequences of Measures on Metric Spaces 8. Seminorms on an Algebra 9. Some Fundamental Identities and Inequalities for Products 10. Convergence in Seminorms of Powers to Infinitely Divisible Elements 11. Convergence in Seminorms of ProductsChapter IV Weak Convergence ON R(k) 1. s-Smooth Measures on R(k) 2. Gaussian Measures and Gaussian Transforms 3. Fourier Transforms and Their Relation to Gaussian Transforms 4. Gaussian Seminorms 5. The Semigroup of s-Smooth Measures 6. Stability Conditions for Convolution Products That Converge Weakly 7. The Unique Divisibility of Infinitely Divisible s-Smooth Measures 8. Lévy Measures on R(k); Gaussian Functionals 9. Weak Convergence of Convolution Powers of s-Smooth Measures 10. The Semigroup of Infinitely Divisible s-Smooth Measures 11. The Characteristic Function of an Infinitely Divisible Probability Measure on R{k) and Its Connection with the Gaussian Functional 12. Weak Convergence of Convolution Products 13. Stable Probability Measures 14. Gaussian Transforms and Gaussian Seminorms of Random Variables: A Comparison Method 15. Weak Limits of Distributions of Sums of Martingale Differences 16. Weak Limits of Distributions of Sums of Random Variables under Independence and f-MixingChapter V Weak Convergence on the C- and D-Spaces 1. The C- and D-Spaces 2. Projections 3. Approximations of Functions by Schauder Sequences 4. Weak Convergence 5. Fluctuations and Weak Convergence 6. Construction of Probability Measures on the C- and D-Spaces 7. Gaussian s-Smooth Measures on the C- and D-Spaces 8. Embedding of Sums of Real-Valued Random Variables in Random Functions into the D-Space 9. Empirical Distribution Functions 10. Embedding of Sequences of Martingale Differences in Random FunctionsChapter VI Weak Convergence in Separable Hilbert Spaces 1. s-Smooth Measures on l2-Space 2. Weak Convergence of Convolution Products of Probability Measures on l2 3.