Bernt Øksendal writes in his book Stochastic Differential Equations (page 11) that a stochastic process is a probability measure $P$ on the measurable space $((\mathbb{R}^n)^T, \mathcal{B})$.
The sample space $(\mathbb{R}^n)^T$ is the set of all functions $f: T \rightarrow \mathbb{R}^n$, and the $\sigma$-algebra $\mathcal{B}$ is generated by the set of functions $\{f: f(t_1) \in F_1, \dots, f(t_k) \in F_k \}$, where $F_i \subset \mathbb{R}^n$ are Borel sets.
I understand the point of view that a stochastic process can be thought of as a realisation from $(\mathbb{R}^n)^T$. And I can accept the definition of the $\sigma$-algebra $\mathcal{B}$. But I'm struggling to grasp why a stochastic process is a probability measure $P$ on the space $((\mathbb{R}^n)^T, \mathcal{B})$.
How does $P(f)$, with $f \in \mathcal{B}$, represent a stochastic process? And how does this representation fulfil axioms such as $0 \leq P \leq 1$?