• chapter 1 Foundations; Set Theory / Richard M. Dudley
• chapter 2 General Topology / Richard M. Dudley
• chapter 3 Measures / Richard M. Dudley
• chapter 4 Integration / Richard M. Dudley
• chapter 5 Lp Spaces; Introduction to Functional Analysis / Richard M. Dudley
• chapter 6 Convex Sets and Duality of Normed Spaces / Richard M. Dudley
• chapter 7 Measure, Topology, and Differentiation / Richard M. Dudley
• chapter 8 Introduction to Probability Theory / Richard M. Dudley
• chapter 9 Convergence of Laws and Central Limit Theorems / Richard M. Dudley
• chapter 10 Conditional Expectations and Martingales / Richard M. Dudley
• chapter 11 Convergence of Laws on Separable Metric Spaces / Richard M. Dudley
• chapter 12 Stochastic Processes / Richard M. Dudley
• chapter 13 Measurability: Borel Isomorphism and Analytic Sets / Richard M. Dudley

Written by one of the best-known probabilists in the world this text offers a clear and modern presentation of modern probability theory and an exposition of the interplay between the properties of metric spaces and those of probability measures. This text is the first at this level to include discussions of the subadditive ergodic theorems, metrics for convergence in laws and the Borel isomorphism theory. The proofs for the theorems are consistently brief and clear and each chapter concludes with a set of historical notes and references. This book should be of interest to students taking degree courses in real analysis and/or probability theory.
(source: Nielsen Book Data)

• 1. Foundations: set theory--
• 2. General topology--
• 3. Measures--
• 4. Integration--
• 5. Lp spaces: introduction to functional analysis--
• 6. Convex sets and duality of normed spaces--
• 7. Measure, topology, and differentiation--
• 8. Introduction to probability theory--
• 9. Convergence of laws and central limit theorems--
• 10. Conditional expectations and martingales--
• 11. Convergence of laws on separable metric spaces--
• 12. Stochastic processes--
• 13. Measurability: Borel isomorphism and analytic sets-- Appendixes: A. Axiomatic set theory-- B. Complex numbers, vector spaces, and Taylor's theorem with remainder-- C. The problem of measure-- D. Rearranging sums of nonnegative terms-- E. Pathologies of compact nonmetric spaces-- Indices.
• (source: Nielsen Book Data)

This classic textbook offers a clear exposition of modern probability theory and of the interplay between the properties of metric spaces and probability measures. The first half of the book gives an exposition of real analysis: basic set theory, general topology, measure theory, integration, an introduction to functional analysis in Banach and Hilbert spaces, convex sets and functions and measure on topological spaces. The second half introduces probability based on measure theory, including laws of large numbers, ergodic theorems, the central limit theorem, conditional expectations and martingale's convergence. A chapter on stochastic processes introduces Brownian motion and the Brownian bridge. The edition has been made even more self-contained than before; it now includes a foundation of the real number system and the Stone-Weierstrass theorem on uniform approximation in algebras of functions. Several other sections have been revised and improved, and the comprehensive historical notes have been further amplified. A number of new exercises have been added, together with hints for solution.
(source: Nielsen Book Data)

• 1. Foundations: set theory--
• 2. General topology--
• 3. Measures--
• 4. Integration--
• 5. Lp spaces: introduction to functional analysis--
• 6. Convex sets and duality of normed spaces--
• 7. Measure, topology, and differentiation--
• 8. Introduction to probability theory--
• 9. Convergence of laws and central limit theorems--
• 10. Conditional expectations and martingales--
• 11. Convergence of laws on separable metric spaces--
• 12. Stochastic processes--
• 13. Measurability: Borel isomorphism and analytic sets-- Appendixes: A. Axiomatic set theory-- B. Complex numbers, vector spaces, and Taylor's theorem with remainder-- C. The problem of measure-- D. Rearranging sums of nonnegative terms-- E. Pathologies of compact nonmetric spaces-- Indices.
• (source: Nielsen Book Data)

This classic textbook offers a clear exposition of modern probability theory and of the interplay between the properties of metric spaces and probability measures. The first half of the book gives an exposition of real analysis: basic set theory, general topology, measure theory, integration, an introduction to functional analysis in Banach and Hilbert spaces, convex sets and functions and measure on topological spaces. The second half introduces probability based on measure theory, including laws of large numbers, ergodic theorems, the central limit theorem, conditional expectations and martingale's convergence. A chapter on stochastic processes introduces Brownian motion and the Brownian bridge. The edition has been made even more self-contained than before; it now includes a foundation of the real number system and the Stone-Weierstrass theorem on uniform approximation in algebras of functions. Several other sections have been revised and improved, and the comprehensive historical notes have been further amplified. A number of new exercises have been added, together with hints for solution.
(source: Nielsen Book Data)

• 1. Foundations: set theory--
• 2. General topology--
• 3. Measures--
• 4. Integration--
• 5. Lp spaces: introduction to functional analysis--
• 6. Convex sets and duality of normed spaces--
• 7. Measure, topology, and differentiation--
• 8. Introduction to probability theory--
• 9. Convergence of laws and central limit theorems--
• 10. Conditional expectations and martingales--
• 11. Convergence of laws on separable metric spaces--
• 12. Stochastic processes--
• 13. Measurability: Borel isomorphism and analytic sets-- Appendixes: A. Axiomatic set theory-- B. Complex numbers, vector spaces, and Taylor's theorem with remainder-- C. The problem of measure-- D. Rearranging sums of nonnegative terms-- E. Pathologies of compact nonmetric spaces-- Indices.
• (source: Nielsen Book Data)

This classic textbook offers a clear exposition of modern probability theory and of the interplay between the properties of metric spaces and probability measures. The first half of the book gives an exposition of real analysis: basic set theory, general topology, measure theory, integration, an introduction to functional analysis in Banach and Hilbert spaces, convex sets and functions and measure on topological spaces. The second half introduces probability based on measure theory, including laws of large numbers, ergodic theorems, the central limit theorem, conditional expectations and martingale's convergence. A chapter on stochastic processes introduces Brownian motion and the Brownian bridge. The edition has been made even more self-contained than before; it now includes a foundation of the real number system and the Stone-Weierstrass theorem on uniform approximation in algebras of functions. Several other sections have been revised and improved, and the comprehensive historical notes have been further amplified. A number of new exercises have been added, together with hints for solution.
(source: Nielsen Book Data)