Stochastic Process

Stochastic processes are necessary ingredients for building models of a wide variety of phenomena exhibiting time varying randomness. In a lively and imaginative presentation, studded with examples, exercises, and applications, and supported by inclusion of computational procedures, the author has created a textbook that provides easy access to this fundamental topic for many students of applied sciences at many levels. With its carefully modularized discussion and crystal clear differentiation between rigorous proof and plausibility argument, it is accessible to beginners but flexible enough to serve as well those who come to the course with strong backgrounds.

Learn or refresh your stochastic calculus with a full lecture, practical examples and 20+ exercises and solutions.

The word stochastic is jargon for random. A stochastic process is a system which evolves in time while undergoing chance fluctuations. We can describe such a system by defining a family of random variables, {Xt}' where Xt measures, at time t, the aspect of the system which is of interest. For example, X t might be the number of customers in a queue at time t. As time passes, customers will arrive and leave, and so the value of Xt will change. At any time t, Xt takes one of the values 0, 1,2, … ; and t can be any value in a subset of ( - 00, 00), the infinite past to the infinite future. If we observe the queue continuously, and customers arrive one at a time to be served by a single server, then, when a customer arrives, the value of Xt' the queue size, increases by one, and when a customer departs after being served, X t decreases by one (Figure 1.1).