Bayesian Parameter Estimation via Filtering and Functional Approximations
Numerical Analysis
2016-11-29 v1 Probability
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.
Cite
@article{arxiv.1611.09293,
title = {Bayesian Parameter Estimation via Filtering and Functional Approximations},
author = {Hermann G. Matthies and Alexander Litvinenko and Bojana V. Rosic and Elmar Zander},
journal= {arXiv preprint arXiv:1611.09293},
year = {2016}
}
Comments
arXiv admin note: text overlap with arXiv:1606.09440