{"id":"palomar/PALOMAR-2026-10-01-000006","url":"https://github.com/burkh4rt/Discriminative-Kalman-Filter-Formalized/tree/6b91b7f967bc4523a3de9024bcb49be1aca42918","json":"https://data.palomar-registry.org/entries/PALOMAR-2026-10-01-000006-v1.json","repo":"https://github.com/burkh4rt/Discriminative-Kalman-Filter-Formalized","tags":["math.PR","stat.TH","msc:60G35","msc:62F15","msc:03B35","kalman-filter","state-estimation","probability","formalization"],"score":0,"title":"Discriminative Kalman Filter Formalized","trust":"high","domain":"math.PR","source":"palomar","authors":["Michael C. Burkhart"],"reviews":0,"subject":"math.PR","summary":"Asymptotic correctness of the discriminative Kalman filter for a stationary linear Gaussian state model with nonlinear and non-Gaussian observations. Formalizes Appendix B, Theorem 2, Lemma 1, and Remarks 1–4 of Burkhart et al. (2020): weak convergence, eventual Gaussian well-definedness, L1 consistency, the exact Gaussian parameter update, the robust update, and the extension to random input distributions with convergence in probability.","category":"research","comments":0,"disputed":false,"division":"math","mentions":0,"verified":true,"discussed":0,"published":"2026-10-01","reactions":{},"formalized":true,"formalizes":[{"title":"The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models","identifier":"https://doi.org/10.1162/neco_a_01275","relationship":"formalizes"}]}