We study a class of stochastic models of mass transport on discrete vertex set V. For these models, a one-parameter family of homogeneous product measures \(\otimes _{i\in V} \nu _\theta \) is reversible. We prove that the set of mixtures of inhomogeneous product measures with equilibrium marginals, i.e., the set of measures of the form \( \int \Big (\bigotimes _{i\in V} \nu _{\theta _i}\Big ) \,\Xi \Big (\prod _{i\in V}d\theta _i\Big ) \) is left invariant by the dynamics in the course of time, and the “mixing measure” \(\Xi \) evolves according to a Markov process which we then call “the hidden parameter model”. This generalizes results from De Masi et al. (Preprint arXiv:2310.01672, 2023) to a larger class of models and on more general graphs. The class of models includes discrete and continuous generalized KMP models, as well as discrete and continuous harmonic models. The results imply that in all these models, the non-equilibrium steady state of their reservoir driven version is a mixture of product measures where the mixing measure is in turn the stationary state of the corresponding “hidden parameter model”. For the boundary-driven harmonic models on the chain \(\{1,\ldots , N\}\) with nearest neighbor edges, we recover that the stationary measure of the hidden parameter model is the joint distribution of the ordered Dirichlet distribution (cf. Carinci et al., Preprint arXiv:2307.14975, 2023), with a purely probabilistic proof based on a spatial Markov property of the hidden parameter model.
