Adapted process

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Stochastic process

title: "Adapted process" type: doc version: 1 created: 2026-02-28 author: "Wikipedia contributors" status: active scope: public tags: ["stochastic-processes"] description: "Stochastic process" topic_path: "general/stochastic-processes" source: "https://en.wikipedia.org/wiki/Adapted_process" license: "CC BY-SA 4.0" wikipedia_page_id: 0 wikipedia_revision_id: 0

::summary Stochastic process ::

In the study of stochastic processes, a stochastic process is adapted (also referred to as a non-anticipating or non-anticipative process) if information about the value of the process at a given time is available at that same time. An informal interpretation is that X is adapted if and only if, for every realisation and every n, Xn is known at time n. The concept of an adapted process is essential, for instance, in the definition of the Itō integral, which only makes sense if the integrand is an adapted process.

Definition

Let

(\Omega, \mathcal{F}, \mathbb{P}) be a probability space;
I be an index set with a total order \leq (often, I is \mathbb{N}, \mathbb{N}_0, [0, T] or [0, +\infty));
\left(\mathcal{F}i\right){i \in I} be a filtration of the sigma algebra \mathcal{F};
(S,\Sigma) be a measurable space, the state space;
X_i: I \times \Omega \to S be a stochastic process.

The stochastic process (X_i)_{i\in I} is said to be adapted to the filtration \left(\mathcal{F}i\right){i \in I} if the random variable X_i: \Omega \to S is a (\mathcal{F}_i, \Sigma)-measurable function for each i \in I.

Examples

Consider a stochastic process X : [0, T] × Ω → R, and equip the real line R with its usual Borel sigma algebra generated by the open sets.

If we take the natural filtration F•X, where FtX is the σ-algebra generated by the pre-images X**s−1(B) for Borel subsets B of R and times 0 ≤ s ≤ t, then X is automatically F•X-adapted. Intuitively, the natural filtration F•X contains "total information" about the behaviour of X up to time t.
This offers a simple example of a non-adapted process X : [0, 2] × Ω → R: set F**t to be the trivial σ-algebra {∅, Ω} for times 0 ≤ t t = FtX for times 1 ≤ t ≤ 2. Since the only way that a function can be measurable with respect to the trivial σ-algebra is to be constant, any process X that is non-constant on [0, 1] will fail to be F•-adapted. The non-constant nature of such a process "uses information" from the more refined "future" σ-algebras F**t, 1 ≤ t ≤ 2.

References

Wiliams, David. (1979). "Diffusions, Markov Processes and Martingales: Foundations". Wiley.
Øksendal, Bernt. (2003). "Stochastic Differential Equations". Springer.

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