Brownian motion is a process of tremendous practical and theoretical significance. It originated (a) as a model of the phenomenon observed by Robert Brown in 1828 that “pollen grains suspended in water perform a continual swarming motion,” and (b) in Bachelier's (1900) work as a model of the stock market. These are just two of many systems that Brownian motion has been used to model. On the theoretical side, Brownian motion is a Gaussian Markov process with stationary independent increments. It lies in the intersection of three important classes of processes and is a fundamental example in each theory.

The first part of this chapter develops properties of Brownian motion. In Section 8.1, we define Brownian motion and investigate continuity properties of its paths. In Section 8.2, we prove the Markov property and a related 0-1 law. In Section 8.3, we define stopping times and prove the strong Markov property. In Section 8.4, we take a close look at the zero set of Brownian motion. In Section 8.5, we introduce some martingales associated with Brownian motion and use them to obtain information about its properties.

The second part of this chapter applies Brownian motion to some of the problems considered in Chapters 2 and 3. In Section 8.6, we embed random walks into Brownian motion to prove Donsker's theorem, a far reaching generalization of the central limit theorem.