Real Analysis and Probability

PrefaceSummary of Notation1 Fundamentals of Measure and Integration Theory 1.1 Introduction 1.2 Fields, s-Fields, and Measures 1.3 Extension of Measures 1.4 Lebesgue-Stieltjes Measures and Distribution Functions 1.5 Measurable Functions and Integration 1.6 Basic Integration Theorems 1.7 Comparison of Lebesgue and Riemann Integrals2 Further Results in Measure and Integration Theory 2.1 Introduction 2.2 Radon-Nikodym Theorem and Related Results 2.3 Applications to Real Analysis 2.4 Lp Spaces 2.5 Convergence Of Sequences of Measurable Functions 2.6 Product Measures and Fubini's Theorem 2.7 Measures on Infinite Product Spaces 2.8 References3 Introduction to Functional Analysis 3.1 Introduction 3.2 Basic Properties of Hilbert Spaces 3.3 Linear Operators on Normed Linear Spaces 3.4 Basic Theorems of Functional Analysis 3.5 Some Properties of Topological Vector Spaces 3.6 References4 The Interplay between Measure Theory and Topology 4.1 Introduction 4.2 The Daniell Integral 4.3 Measures on Topological Spaces 4.4 Measures on Uncountably Infinite Product Spaces 4.5 Weak Convergence of Measures 4.6 References5 Basic Concepts of Probability 5.1 Introduction 5.2 Discrete Probability Spaces 5.3 Independence 5.4 Bernoulli Trials 5.5 Conditional Probability 5.6 Random Variables 5.7 Random Vectors 5.8 Independent Random Variables 5.9 Some Examples from Basic Probability 5.10 Expectation 5.11 Infinite Sequences of Random Variables 5.12 References6 Conditional Probability and Expectation 6.1 Introduction 6.2 Applications 6.3 The General Concept of Conditional Probability and Expectation 6.4 Conditional Expectation, Given a s-Field 6.5 Properties Of Conditional Expectation 6.6 Regular Conditional Probabilities 6.7 References7 Strong Laws of Large Numbers and Martingale Theory 7.1 Introduction 7.2 Convergence Theorems 7.3 Martingales 7.4 Martingale Convergence Theorems 7.5 Uniform Integrability 7.6 Uniform Integrability and Martingale Theory 7.7 Optional Sampling Theorems 7.8 Applications of Martingale Theory 7.9 Applications To Markov Chains 7.10 References8 The Central Limit Theorem 8.1 Introduction 8.2 The Fundamental Weak Compactness Theorem 8.3 Convergence to a Normal Distribution 8.4 Stable Distributions 8.5 Infinitely Divisible Distributions 8.6 Uniform Convergence in the Central Limit Theorem 8.7 Proof of the Inversion Formula (8.1.4) 8.8 Completion of the Proof of Theorem 8.3.2 8.9 Proof of the Convergence of Types Theorem (8.3.4) 8.10 ReferencesAppendix on General Topology A1 Introduction A2 Convergence A3 Product and Quotient Topologies A4 Separation Properties and Other Ways of Classifying Topological Spaces A5 Compactness A6 Semicontinuous Functions A7 The Stone-Weierstrass Theorem A8 Topologies on Function Spaces A9 Complete Metric Spaces and Category Theorems A10 Uniform SpacesBibliographySolutions to ProblemsSubject Index