# Imprecise Markov Semigroups and their Ergodicity

arXiv: https://arxiv.org/abs/2405.00081 · HTML: https://mlsanity.com/paper/2405.00081/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 65 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models](https://mlsanity.com/paper/2302.10341/) (2023)
- [Approximating Euclidean by Imprecise Markov Decision Processes](https://mlsanity.com/paper/2006.14923/) (2020)

## Built on by (most cited first, 3 of 3)

- [Conformalized Credal Regions for Classification with Ambiguous Ground Truth](https://mlsanity.com/paper/2411.04852/) (2024)
- [Conformal Prediction Regions are Imprecise Highest Density Regions](https://mlsanity.com/paper/2502.06331/) (2025)
- [Optimal Transport for ε-Contaminated Credal Sets: To the Memory of Sayan Mukherjee](https://mlsanity.com/paper/2410.03267/) (2024)

## Abstract (arXiv)

We introduce the concept of an imprecise Markov semigroup (mathbf Q). It is a tool that allows us to represent ambiguity around both the transition probabilities and the invariant measure of a continuous-time Markov process via a collection of Markov semigroups, each associated with a (possibly different) Markov process. We use techniques from topology, geometry, and probability to analyze ergodic limits under model uncertainty encoded by (mathbf Q). We establish long-term bounds that are uniform in the initial state and identify regimes in which the imprecision in these bounds collapses asymptotically. Our results are proved in progressively more general settings. We first assume that (mathbf Q) is compact and that the state space is Euclidean or a Riemannian manifold, working with a fixed bounded observable. We then allow the state space to be standard Borel, while keeping (mathbf Q) compact and the observable fixed. Finally, we drop compactness and work on Polish metric spaces of finite diameter, where we treat arbitrary bounded Lipschitz observables. The importance of our findings for the fields of artificial intelligence and computer vision is also discussed at a high level; In particular, in the study of how the probability of an output evolves over time as we perturb the input of a convolutional autoencoder.