An introduction to stochastic process

This book, suitable for advanced undergraduate, postgraduate and research courses in statistics, applied mathematics, operation research, computer science, different branches of engineering, business and management, economics and life sciences etc., is aimed between elementary probability texts and advanced works on stochastic processes. What distinguishes the text is the illustration of the theorems by examples and applications. Table of Contents Preface Abbreviations and Notations Introduction Discrete Time Markov Chain Random Walks Renewal Theory Branching Process Continuous Time Discrete State Markov Processes Poisson Process Continuous Time and Continuous State Markov Process Time Series Analysis Queuing Theory Sample paths of Brownian notions Second order stochastic Analysis Stochastic integral (Ito intergal) Some important martingale theorems Solutions of Problems and Complements. Loosely speaking, the mathematical description of a random phenomenon as it changes in time is a stochastic process.