A statistical model is a function $P : \Theta \to \Delta(X)$, where $\Theta$ is a parameter space, and $\Delta(X)$ is the set of probability measures on a state space $X$.

Suppose that $\Theta$ and $X$ are topological spaces, and equip $\Delta(X)$ with the topology of weak convergence of measures. Suppose that the function $P : \Theta \to \Delta(X)$ is continuous, defining a topological statistical model.

An $X$-valued random variable is a measurable function $x : \Omega \to X$ on some abstract probability space $(\Omega, \mathcal F, \mathbb P)$. The push-forward measure $x_* \mathbb P := \mathbb P \circ x^{-1}$ is called the law or marginal of $x$.

If $P : \Theta \to \Delta(X)$ defines a statistical model, can we find an abstract probability space $(\Omega, \mathcal F, \mathbb P)$ and a family of $X$-valued random variables $\{x_\theta\}$ so that $(x_\theta)_* \mathbb P = P_\theta$ for each $\theta \in \Theta$?

Moreover, if $P$ defines a topological statistical model, what regularity properties does this imply about the higher-order function $\theta \mapsto x_\theta$? If so, can the space $L = L(\Omega,X)$ of $X$-valued random variables be equipped with a natural topology so that the function $x : \Theta \to L$ is continuous?