Introduction to Probability ModelsIntroduction to Probability Models, Tenth Edition, provides an introduction to elementary probability theory and stochastic processes. There are two approaches to the study of probability theory. One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text. The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic processes, including Markov chains and Poison processes. The remaining chapters cover queuing, reliability theory, Brownian motion, and simulation. Many examples are worked out throughout the text, along with exercises to be solved by students. This book will be particularly useful to those interested in learning how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. Ideally, this text would be used in a oneyear course in probability models, or a onesemester course in introductory probability theory or a course in elementary stochastic processes. New to this Edition:
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This book is great. I had two classes with Sheldon Ross at USC. He is really smart and this book is very comprehensive. Complex topics are covered very clearly.
Review: Introduction to Probability Models
User Review  Jette Stuart  GoodreadsSheldon Ross is a genius of our time. This is an excellent book for introduction to stochastic processes, a subject that I am sure most find challenging. Read full review
Contents
Chapter 3 Conditional Probability and Conditional Expectation  97 
Chapter 4 Markov Chains  191 
Chapter 5 The Exponential Distribution and the Poisson Process  291 
Chapter 6 ContinuousTime Markov Chains  371 
Chapter 7 Renewal Theory and Its Applications  421 
Chapter 8 Queueing Theory  497 
Common terms and phrases
algorithm amount average number balls binomial Brownian motion busy period coin components compute conditional distribution conditional probability Consider continuous random variables continuoustime Markov chain customers arrive cycle define denote the number density function determine distributed with mean distribution F equal Equation Example expected number expected value exponential random variables exponential with rate exponentially distributed flips follows given Hence identically distributed independent and identically independent exponential random independent random variables Let Xn limiting probabilities longrun proportion machine Markov chain nodes normal random variable number of customers obtain outcome Poisson distributed Poisson random variable preceding probability mass function process with rate Proposition queueing model random number recurrent renewal process result sequence server simulate Solution stationary stochastic process Suppose theorem transition probabilities trials Var(X variable with mean variable with parameter variance vector yields