In mathematics, a sample-continuous process is a stochastic process whose sample paths are almost surely continuous functions.
Definition Let (Ω, Σ, P) be a probability space. Let X : I × Ω → S be a stochastic process, where the index set I and state space S are both topological spaces. Then the process X is called sample-continuous (or almost surely continuous, or simply continuous) if the map X(ω) : I → S is continuous as a function of topological spaces for P-almost all ω in Ω. In many examples, the index set I is an interval of time, [0, T] or [0, +∞), and the state space S is the real line or n-dimensional Euclidean space Rn.
Examples Brownian motion (the Wiener process) on Euclidean space is sample-continuous. For "nice" parameters of the equations, solutions to stochastic differential equations are sample-continuous. See the existence and uniqueness theorem in the stochastic differential equations article for some sufficient conditions to ensure sample continuity. The process X : [0, +∞) × Ω → R that makes equiprobable jumps up or down every unit time according to
{ X t ∼ U n i f ( { X t − 1 − 1 , X t − 1 + 1 } ) , t an integer; X t = X ⌊ t ⌋ , t not an integer; {\displaystyle {\begin{cases}X_{t}\sim \mathrm {Unif} (\{X_{t-1}-1,X_{t-1}+1\}),&t{\mbox{ an integer;}}\\X_{t}=X_{\lfloor t\rfloor },&t{\mbox{ not an integer;}}\end{cases}}}
is not sample-continuous. In fact, it is surely discontinuous.
Properties For sample-continuous processes, the finite-dimensional distributions determine the law, and vice versa.
See also Continuous stochastic process
References Kloeden, Peter E.; Platen, Eckhard (1992). Numerical solution of stochastic differential equations. Applications of Mathematics (New York) 23. Berlin: Springer-Verlag. pp. 38–39. ISBN 3-540-54062-8.
