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 ⊗i∈Vνθ 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 (Formula presented.) is left invariant by the dynamics in the course of time, and the “mixing measure” Ξ 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,…,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.

Intertwining and Propagation of Mixtures for Generalized KMP Models and Harmonic Models / Giardina', C.; Redig, F.; van Tol, B.. - In: JOURNAL OF STATISTICAL PHYSICS. - ISSN 0022-4715. - 192:2(2025), pp. 1-33. [10.1007/s10955-025-03393-1]

Intertwining and Propagation of Mixtures for Generalized KMP Models and Harmonic Models

Giardina' C.;Redig F.;
2025

Abstract

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 ⊗i∈Vνθ 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 (Formula presented.) is left invariant by the dynamics in the course of time, and the “mixing measure” Ξ 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,…,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.
2025
192
2
1
33
Intertwining and Propagation of Mixtures for Generalized KMP Models and Harmonic Models / Giardina', C.; Redig, F.; van Tol, B.. - In: JOURNAL OF STATISTICAL PHYSICS. - ISSN 0022-4715. - 192:2(2025), pp. 1-33. [10.1007/s10955-025-03393-1]
Giardina', C.; Redig, F.; van Tol, B.
File in questo prodotto:
File Dimensione Formato  
s10955-025-03393-1.pdf

Open access

Tipologia: VOR - Versione pubblicata dall'editore
Dimensione 499.19 kB
Formato Adobe PDF
499.19 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1377803
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact