Topics in Stochastic Processes covers specific processes that have a definite physical interpretation and that explicit numerical results can be obtained. This book contains five chapters and begins with the L2 stochastic processes and the concept of prediction theory. The next chapter discusses the principles of ergodic theorem to real analysis, Markov chains, and information theory. Another chapter deals with the sample function behavior of continuous parameter processes. This chapter also explores the general properties of Martingales and Markov processes, as well as the one-dimensional Brownian motion. The aim of this chapter is to illustrate those concepts and constructions that are basic in any discussion of continuous parameter processes, and to provide insights to more advanced material on Markov processes and potential theory. The final chapter demonstrates the use of theory of continuous parameter processes to develop the Itô stochastic integral. This chapter also provides the solution of stochastic differential equations.This book will be of great value to mathematicians, engineers, and physicists.

PrefaceChapter 1 L2 Stochastic Processes 1.1 Introduction 1.2 Covariance Functions 1.3 Second Order Calculus 1.4 Karhunen-Loève Expansion 1.5 Estimation Problems 1.6 NotesChapter 2 Spectral Theory and Prediction 2.1 Introduction; L2 Stochastic Integrals 2.2 Decomposition of Stationary Processes 2.3 Examples of Discrete Parameter Processes 2.4 Discrete Parameter Prediction: Special Cases 2.5 Discrete Parameter Prediction: General Solution 2.6 Examples of Continuous Parameter Processes 2.7 Continuous Parameter Prediction in Special Cases; Yaglom's Method 2.8 Some Stochastic Differential Equations 2.9 Continuous Parameter Prediction: Remarks on the General Solution 2.10 NotesChapter 3 Ergodic Theory 3.1 Introduction 3.2 Ergodicity and Mixing 3.3 The Pointwise Ergodic Theorem 3.4 Applications to Real Analysis 3.5 Applications to Markov Chains 3.6 The Shannon-McMillan Theorem 3.7 NotesChapter 4 Sample Function Analysis of Continuous Parameter Stochastic Processes 4.1 Separability 4.2 Measurability 4.3 One-Dimensional Brownian Motion 4.4 Law of the Iterated Logarithm 4.5 Markov Processes 4.6 Processes with Independent Increments 4.7 Continuous Parameter Martingales 4.8 The Strong Markov Property 4.9 NotesChapter 5 The Itô Integral and Stochastic Differential Equations 5.1 Definition of the Itô Integral 5.2 Existence and Uniqueness Theorems for Stochastic Differential Equations 5.3 Stochastic Differentials: A Chain Rule 5.4 NotesAppendix 1 Some Results from Complex AnalysisAppendix 2 Fourier Transforms on the Real LineReferencesSolutions to ProblemsIndex