Robust Sequential Tracking via Bounded Information Geometry and Non-Parametric Field Actions
Standard sequential inference architectures are compromised by a normalizability crisis when confronted with extreme, structured outliers. By operating on unbounded parameter spaces, state-of-the-art estimators lack the intrinsic geometry required to appropriately sever anomalies, resulting in unbounded covariance inflation and mean divergence. This paper resolves this structural failure by analyzing the abstraction sequence of inference at the meta-prior level (S_2). We demonstrate that extremizing the action over an infinite-dimensional space requires a non-parametric field anchored by a pre-prior, as a uniform volume element mathematically does not exist. By utilizing strictly invarian...
| Authors | Carlos C. Rodriguez |
|---|---|
| Primary category | stat.ML |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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