A variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.DS 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Reinforcement Learning, Optimal Control, and Bayesian Filtering in Data Assimilation
A variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.