Bowker Data Service Summary
This text is designed for a one-year course in probability and stochastic processes with applications, especially for students who wish to specialize in probabilistic modeling.
Long Description
An easily accessible, real-world approach to probability and stochastic processes Introduction to Probability and Stochastic Processes with Applications presents a clear, easy-to-understand treatment of probability and stochastic processes, providing readers with a solid foundation they can build upon throughout their careers. With an emphasis on applications in engineering, applied sciences, business and finance, statistics, mathematics, and operations research, the book features numerous real-world examples that illustrate how random phenomena occur in nature and how to use probabilistic techniques to accurately model these phenomena. The authors discuss a broad range of topics, from the basic concepts of probability to advanced topics for further study, including Itô integrals, martingales, and sigma algebras. Additional topical coverage includes: Distributions of discrete and continuous random variables frequently used in applications Random vectors, conditional probability, expectation, and multivariate normal distributions The laws of large numbers, limit theorems, and convergence of sequences of random variables Stochastic processes and related applications, particularly in queueing systems Financial mathematics, including pricing methods such as risk-neutral valuation and the Black-Scholes formula Extensive appendices containing a review of the requisite mathematics and tables of standard distributions for use in applications are provided, and plentiful exercises, problems, and solutions are found throughout. Also, a related website features additional exercises with solutions and supplementary material for classroom use. Introduction to Probability and Stochastic Processes with Applications is an ideal book for probability courses at the upper-undergraduate level. The book is also a valuable reference for researchers and practitioners in the fields of engineering, operations research, and computer science who conduct data analysis to make decisions in their everyday work.
Main Description
This book develops the basic concepts of probability, random variables, standard discrete and continuous distributions, joint probability distributions, laws of large numbers, the central limit theorem, and financial mathematics. Statistical inference and its application in stochastic processes, in particular to queueing systems, is also addressed. The presented theory is illustrated with many current real world examples and applications. This book reinforces and extends the material typically covered in introductory probability courses and is specifically written and accessible for an introductory probability course including students with diverse backgrounds and majoring in engineering, the applied sciences, business and finance, statistics, mathematics, and/or operations research. Chapter coverage includes: basic concepts; random variables and their distributions; discrete distributions; continuous distributions; random vectors; functions and multivariate normal distributions; conditional expected value and martingales; limit theorems; statistical inference; stochastic calculus; and mathematical calculus.
Main Description
This text book is designed for a one-year course in probability and stochastic processes with applications, especially for students who wish to specialize in probabilistic modeling. This book bridges the gap between elementary texts and advanced texts in probability and is easily accessible for students with diverse backgrounds and majoring in engineering, applied sciences, business and finance, statistics, mathematics, and operations research. The text contains many examples and exercises which have been tested in classrooms and are chosen from diverse areas such as queuing models, reliability and finance. Chapter coverage includes: basic concepts; random variables and their distributions; discrete distributions; continuous distributions; random vectors; multivariate normal distributions; conditional expectation; limit theorems; stochastic processes; queuing models; stochastic calculus; and mathematical finance.