On a probability space , a standard one-dimensional Brownian motion is a real-valued stochastic process with almost surely, continuous sample paths almost surely, and independent increments, such thatHere the second parameter is the variance. Thus its Brownian increments have the normal distribution with mean zero and variance equal to elapsed time. Equivalently, relative to a specified filtration , require to be an adapted process and to be independent of with this normal distribution. Taking the natural Brownian filtration recovers the first definition.
In the Lévy construction of Brownian motion, start with and , where has the standard normal distribution. Suppose values are defined at the endpoints and . Setusing fresh independent standard normal random variables , also independent of everything previously used. Join the values by straight segments to obtain a continuous piecewise linear process on the dyadic grid.
To verify the Brownian increments, write and . The two new increments are and . If , their variances are and their covariance is zero. They are Jointly Gaussian, so they are independent. Induction proves the required Brownian increments on every dyadic grid.
For convergence, successive interpolants differ by tents of disjoint interiors, soThe Gaussian tail bound and the union bound giveThese probabilities are summable. The First Borel-Cantelli lemma and therefore prove uniform convergence of the interpolants almost surely. Their limit has continuous sample paths. Approximating any finite set of times by dyadic times passes the Jointly Gaussian increment laws to the limit, giving a standard Brownian motion on .
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