A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation
Jonathan R. Stroud, Matthias Katzfuss, Christopher K. Wikle
Georgetown University, Texas A&M University, University of Missouri
This paper proposes new methodology for sequential state and parameter estimation within
the ensemble Kalman filter. The method is fully Bayesian and propagates the joint posterior
density of states and parameters over time. In order to implement the method we consider
two representations of the marginal posterior distribution of the parameters: a grid-based
approach and a Gaussian approximation. Contrary to existing algorithms, the new method
explicitly accounts for parameter uncertainty and provides a formal way to combine information
about the parameters from data at a different time periods. The method is illustrated and compared
to existing approaches using simulated and real data.
The manuscript is available in PDF format.