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arXiv · 1611.09293

Bayesian Parameter Estimation via Filtering and Functional Approximations

Hermann G. Matthies·Alexander Litvinenko·Bojana V. Rosic·Elmar Zander
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Abstract

The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.

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Hermann G. Matthies, Alexander Litvinenko, Bojana V. Rosic, Elmar Zander. 2016-11-25. Bayesian Parameter Estimation via Filtering and Functional Approximations. https://arxiv.org/abs/1611.09293

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