<P> A stochastic process is defined as a collection of random variables defined on a common probability space (Ω, F, P) (\ displaystyle (\ Omega, (\ mathcal (F)), P)), where Ω (\ displaystyle \ Omega) is a sample space, F (\ displaystyle (\ mathcal (F))) is a σ (\ displaystyle \ sigma) - algebra, and P (\ displaystyle P) is a probability measure, and the random variables, indexed by some set T (\ displaystyle T), all take values in the same mathematical space S (\ displaystyle S), which must be measurable with respect to some σ (\ displaystyle \ sigma) - algebra Σ (\ displaystyle \ Sigma). </P> <P> In other words, for a given probability space (Ω, F, P) (\ displaystyle (\ Omega, (\ mathcal (F)), P)) and a measurable space (S, Σ) (\ displaystyle (S, \ Sigma)), a stochastic process is a collection of S (\ displaystyle S) - valued random variables, which can be written as: </P> <P> Historically, in many problems from the natural sciences a point t ∈ T (\ displaystyle t \ in T) had the meaning of time, so X (t) (\ displaystyle X (t)) is random variable representing a value observed at time t (\ displaystyle t). A stochastic process can also be written as (X (t, ω): t ∈ T) (\ displaystyle \ (X (t, \ omega): t \ in T \)) to reflect that it is actually a function of two variables, t ∈ T (\ displaystyle t \ in T) and ω ∈ Ω (\ displaystyle \ omega \ in \ Omega). </P> <P> There are others ways to consider a stochastic process, with the above definition being considered the traditional one . For example, a stochastic process can be interpreted or defined as a S T (\ displaystyle S ^ (T)) - valued random variable, where S T (\ displaystyle S ^ (T)) is the space of all the possible S (\ displaystyle S) - valued functions of t ∈ T (\ displaystyle t \ in T) that map from the set T (\ displaystyle T) into the space S (\ displaystyle S). </P>

The process of finding components of a force is called