Multi-Parameter Processes:
An Introduction to Random Fields

By Davar Khoshnevisan

Table of Contents:

Part I: Discrete-Parameter Random Fields

Chapter 1. Discrete Parameter Martingales

  1. One-Parameter Martingales
  2. Ortho-Martingales
  3. Martingales
  4. Supplementary Exercises
  5. Notes on Chapter 1

Chapter 2. Two Applications in Analysis

  1. Haar Systems
  2. Differentiation
  3. Supplementary Exercises
  4. Notes on Chapter 2

Chapter 3. Random Walks

  1. One-Parameter Random Walks
  2. Intersection Probabilities
  3. The Simple Random Walk
  4. Supplementary Exercises
  5. Notes on Chapter 3

Chapter 4. Multiparameter Random Walks

  1. The Strong Law of Large Numbers
  2. The Law of the Iterated Logarithm
  3. Supplementary Exercises
  4. Notes on Chapter 4

Chapter 5. Gaussian Random Variables

  1. The Basic Construction
  2. Regularity Theory
  3. Standard Brownian Sheet
  4. Supplementary Exercises
  5. Notes on Chapter 5

Chapter 6. Limit Theorems

  1. Random Variables
  2. Weak Convergence
  3. The Space C
  4. Invariance Principles
  5. Supplementary Exercises
  6. Notes on Chapter 6

Part II: Continuous-Parameter Random Fields

Chapter 7. Continuous Parameter Martingales

  1. One-Parameter Martingales
  2. Multiparameter Martingales
  3. One-Parameter Stochastic Integration
  4. Stochastic Partial Differential Equations
  5. Supplementary Exercises
  6. Notes on Chapter 7

Chapter 8. Constructing Markov Processs

  1. Discrete Markov Chains
  2. Markov Semigroups
  3. Markov Processes
  4. Feller Processes
  5. Supplementary Exercises
  6. Notes on Chapter 8

Chapter 9. Generation of Markov Processes

  1. Generation
  2. Explicit Computations
  3. The Feynman-Kac Formula
  4. Exit Times and Brownian Motion
  5. Supplementary Exercises
  6. Notes on Chapter 9

Chapter 10. Probabilistic Potential Theory

  1. Recurrent Lévy Processes
  2. Hitting Probabilities for Feller Processes
  3. Explicit Computations
  4. Supplementary Exercises
  5. Notes on Chapter 10

Chapter 11. Multiparameter Markov Processes

  1. Definitions
  2. Examples
  3. Potential Theory
  4. Applications
  5. Alpha-Regular Gaussian Random Fields
  6. Supplementary Exercises
  7. Notes on Chapter 11

Chapter 12. Brownian Sheet and Potential Theory

  1. Polar Sets of the Range of Brownian Sheet
  2. The Codimension of the Level Sets
  3. Local Times as Frostman's Measures
  4. Supplementary Exercises
  5. Notes on Chapter 12

Part III: Appendices

  1. Kolmogorov's Consistency Theorem
  2. Laplace Transforms
  3. Hausdorff Dimensions and Measures
  4. Energy and Capacity

Bibliography

References

Subject Index

Name Index