Probability and Stochastic Processes

<p><b>A comprehensive and accessible presentation of probability and stochastic processes with emphasis on key theoretical concepts and real-world applications</b><b><br /> <br /> </b>With a sophisticated approach, <i>Probability and Stochastic Processes</i> successfully balances theory and applications in a pedagogical and accessible format. The book’s primary focus is on key theoretical notions in probability to provide a foundation for understanding concepts and examples related to stochastic processes.<br /> <br /> Organized into two main sections, the book begins by developing probability theory with topical coverage on probability measure; random variables; integration theory; product spaces, conditional distribution, and conditional expectations; and limit theorems. The second part explores stochastic processes and related concepts including the Poisson process, renewal processes, Markov chains, semi-Markov processes, martingales, and Brownian motion. Featuring a logical combination of traditional and complex theories as well as practices, <i>Probability and Stochastic Processes</i> also includes:<br /> <br /> </p> <ul> <li>Multiple examples from disciplines such as business, mathematical finance, and engineering</li> <li>Chapter-by-chapter exercises and examples to allow readers to test their comprehension of the presented material</li> <li>A rigorous treatment of all probability and stochastic processes concepts</li> </ul> <br /> An appropriate textbook for probability and stochastic processes courses at the upper-undergraduate and graduate level in mathematics, business, and electrical engineering, <i>Probability and Stochastic Processes</i> is also an ideal reference for researchers and practitioners in the fields of mathematics, engineering, and finance.