On a measurable space $(E,\mathcal E)$, a stochastic kernel is a function $p\colon E\times \mathcal E\to [0,1]$ such that:
- for each $x\in E$, the function $A\mapsto p(x,A)$ is a probability measure;
- for each $A\in\mathcal E$, the function $x\mapsto p(x,A)$ is measurable.
Such a kernel induces a linear operator $P$ on the space $\mathcal B(E)$ of bounded measurable functions $f\colon E\to\mathbb R$ as follows: $$\forall x\in E,\qquad Pf(x):=\int f(y)p(x,dy).$$ Clearly, the linear operator $P\colon \mathcal B(E)\to \mathcal B(E)$ preserves non-negativity and maps the constant function $1$ to itself.
My question is about the converse: is it true that any linear operator $P\colon \mathcal B(E)\to \mathcal B(E)$ preserving non-negativity and mapping $1$ to itself arises in this way ? Of course, the underlying stochastic kernel then has to be $$\forall(x,A)\in E\times\mathcal E,\qquad p(x,A) := P1_A(x),$$ but proving $\sigma-$additivity seems to require extra assumptions on the ambient space $(E,\mathcal E)$, and I was unable to find a reference.