# Fluctuations of the Empirical Measure of Freezing Markov Chains

## TL;DR

This paper studies the long-term behavior of empirical measures in a class of freezing Markov chains with decreasing transition probabilities, extending existing results to more general freezing speeds and providing detailed convergence characterizations.

## Contribution

It generalizes previous convergence results for freezing Markov chains to arbitrary freezing speeds using stochastic approximation, offering improved limit distribution descriptions and convergence rates.

## Key findings

- Generalized convergence results for any freezing speed
- Characterized limit distributions and convergence rates
- Provided functional convergence analysis

## Abstract

In this work, we consider a finite-state inhomogeneous-time Markov chain whose probabilities of transition from one state to another tend to decrease over time. This can be seen as a cooling of the dynamics of an underlying Markov chain. We are interested in the long time behavior of the empirical measure of this freezing Markov chain. Some recent papers provide almost sure convergence and convergence in distribution in the case of the freezing speed $n^{-\theta}$, with different limits depending on $\theta<1,\theta=1$ or $\theta>1$. Using stochastic approximation techniques, we generalize these results for any freezing speed, and we obtain a better characterization of the limit distribution as well as rates of convergence as well as functional convergence.