# Law (stochastic processes)

In mathematics, the law of a stochastic process is the measure that the process induces on the collection of functions from the index set into the state space. The law encodes a lot of information about the process; in the case of a random walk, for example, the law is the probability distribution of the possible trajectories of the walk.

## Definition

Let (Ω, FP) be a probability space, T some index set, and (S, Σ) a measurable space. Let X : T × Ω → S be a stochastic process (so the map

$X_{t} : \Omega \to S : \omega \mapsto X (t, \omega)$

is a (F, Σ)-measurable function for each t ∈ T). Let ST denote the collection of all functions from T into S. The process X (by way of currying) induces a function ΦX : Ω → ST, where

$\left( \Phi_{X} (\omega) \right) (t) := X_{t} (\omega).$

The law of the process X is then defined to be the pushforward measure

$\mathcal{L}_{X} := \left( \Phi_{X} \right)_{*} ( \mathbf{P} ) = \mathbf P \circ \Phi_X^{-1}$

on ST.

## Example

• The law of standard Brownian motion is classical Wiener measure. (Indeed, many authors define Brownian motion to be a sample continuous process starting at the origin whose law is Wiener measure, and then proceed to derive the independence of increments and other properties from this definition; other authors prefer to work in the opposite direction.)