Empirical Transfer Operators and Finite-Sample Change Detection for Noisy Expanding Interval Maps
We study finite-sample change detection for one-dimensional noisy dynamical systems using partition-based empirical approximations of stationary behaviour. Given observations from an interval-valued process, we partition the state space, estimate a finite transition matrix from observed transitions between partition elements, and apply a small Doeblin-type regularisation to ensure a unique stationary distribution. From an initial reference segment, we compute a baseline empirical stationary distribution \(\widehatπ_{0,ρ}\). For each later sliding window, we compute \(\widehatπ_{t,ρ}\) and define the score \[ S_t=\|\widehatπ_{t,ρ}-\widehatπ_{0,ρ}\|_1. \] Large values of \(S_t\) indicate a ...
| Authors | Aparna Rajput |
|---|---|
| Primary category | stat.ML |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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