Online Generalised Predictive Coding
This paper introduces an extension of generalised filtering for online applications. Generalised filtering refers to data assimilation schemes that jointly infer latent states, learn unknown model parameters, and estimate uncertainty in an integrated framework -- e.g., estimate state and observation noise -- at the same time (i.e., triple estimation). This framework appears across disciplines under different names, including variational Kalman-Bucy filtering in engineering, generalised predictive coding in neuroscience, and Dynamic Expectation Maximisation (DEM) in time-series analysis. Here, we specialise DEM for ``online'' data assimilation, through a separation of temporal scales. We d...
| Authors | Mehran H. Z. Bazargani, Szymon Urbas, Adeel Razi et al. |
|---|---|
| Primary category | stat.ML |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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