The Score Kalman Filter
A central obstacle in nonlinear Bayesian filtering is representing the belief distribution. Moment-based filters address this by propagating polynomial moments and reconstructing a density from them. Recent work completes the predict-update loop via the maximum-entropy (MaxEnt) principle, but each step requires the partition function and its gradient, both $n$-dimensional integrals whose cost scales exponentially, restricting the demonstrated MaxEnt moment filtering to $n \le 4$. We avoid the partition function entirely by combining score matching with Stein's identity. In our setting, score matching reduces the density fit to a single linear solve whose coefficients are assembled directl...
| Authors | Kaito Iwasaki, Anthony Bloch, Taeyoung Lee et al. |
|---|---|
| Primary category | eess.SY |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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