Doubly Outlier-Robust Online Infinite Hidden Markov Model
We derive a robust update rule for the online infinite hidden Markov model (iHMM) for when the streaming data contains outliers and the model is misspecified. Leveraging recent advances in generalised Bayesian inference, we define robustness via the posterior influence function (PIF), and provide conditions under which the online iHMM has bounded PIF. Imposing robustness inevitably induces an adaptation lag for regime switching. Our method, which is called Batched Robust iHMM (BR-iHMM), balances adaptivity and robustness with two additional tunable parameters. Across limit order book data, hourly electricity demand, and a synthetic high-dimensional linear system, BR-iHMM reduces one-step-...
| Authors | Horace Yiu, Leandro Sánchez-Betancourt, Álvaro Cartea et al. |
|---|---|
| Primary category | stat.ML |
| Published | 15 April 2026 |
| Last updated at source | 12 May 2026 |
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