# Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference

@article{Qian2022ModelRO, title={Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference}, author={Elizabeth Qian and Jemima M. Tabeart and Christopher A. Beattie and Serkan Gugercin and Jiahua Jiang and Peter R. Kramer and Akil C. Narayan}, journal={Journal of Scientific Computing}, year={2022}, volume={91}, pages={1-30} }

We consider the Bayesian approach to the linear Gaussian inference problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken after the initial time. In practical applications, the large dimension of the dynamical system state poses a computational obstacle to computing the exact posterior distribution. Model reduction offers a variety of computational tools that seek to reduce this computational burden. In particular, balanced truncation is a…

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