Mixing properties of some Markov chains models in random environments
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916958294769664 |
|---|---|
| author | Lovas, Attila Truquet, Lionel |
| author_facet | Lovas, Attila Truquet, Lionel |
| contents | Markov chains in random environments (MCREs) have recently attracted renewed interest, as these processes naturally arise in many applications, such as econometrics and machine learning. Although specific asymptotic results, such as the law of the large numbers and central limit theorems, have been obtained for some of these models, their annealed dependence properties, such as strong mixing properties, are not well understood in general. We derive strong mixing properties for a wide range of MCREs that satisfy some drift/small set conditions, with general assumptions on the corresponding stochastic parameters and the mixing properties of the environments. We then demonstrate the wide range of applications of our results in time series analysis and stochastic gradient Langevin dynamics, with fewer restrictions than those found in existing literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16118 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mixing properties of some Markov chains models in random environments Lovas, Attila Truquet, Lionel Probability Statistics Theory Markov chains in random environments (MCREs) have recently attracted renewed interest, as these processes naturally arise in many applications, such as econometrics and machine learning. Although specific asymptotic results, such as the law of the large numbers and central limit theorems, have been obtained for some of these models, their annealed dependence properties, such as strong mixing properties, are not well understood in general. We derive strong mixing properties for a wide range of MCREs that satisfy some drift/small set conditions, with general assumptions on the corresponding stochastic parameters and the mixing properties of the environments. We then demonstrate the wide range of applications of our results in time series analysis and stochastic gradient Langevin dynamics, with fewer restrictions than those found in existing literature. |
| title | Mixing properties of some Markov chains models in random environments |
| topic | Probability Statistics Theory |
| url | https://arxiv.org/abs/2509.16118 |