Wiener process
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In mathematics, the Wiener process, so named in honor of Norbert Wiener, is a continuous-time Gaussian stochastic process with independent increments used in modelling Brownian motion and some random phenomena observed in finance. It is one of the best-known Lévy processes. For each positive number t, denote the value of the process at time t by Wt. Then the process is characterized by the following two conditions:
- If 0 < s < t, then
- <math>W_t-W_s\sim N(0,\sigma^2(t-s))<math>
- ("N(μ, σ2)" denotes the normal distribution with expected value μ and variance σ2.)
- If 0 ≤ s ≤ t ≤ u ≤ v, (i.e., the two intervals [s, t] and [u, v] do not overlap) then
- <math>W_t-W_s\ \mbox{and}\ W_v-W_u<math>
- are independent random variables, and similarly for more than two non-overlapping intervals.
The paths are almost surely continuous. The Wiener measure is the probability law on the space of continuous functions g, with g(0) = 0, induced by the Wiener process. An integral based on Wiener measure may be called a Wiener integral.
The conditional probability distribution of the Wiener process given that W(0) = W(1) = 0 is called a Brownian bridge.de:Wiener-Prozess