AI RESEARCH
Doubly Outlier-Robust Online Infinite Hidden Markov Model
arXiv CS.LG
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ArXi:2604.14322v1 Announce Type: cross We derive a robust update rule for the online infinite hidden Marko 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.