Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference

نویسندگان

چکیده

We consider the Bayesian approach to linear Gaussian inference problem of inferring initial condition a dynamical system from noisy output measurements taken after time. In practical applications, large dimension state poses computational obstacle computing exact posterior distribution. Model reduction offers variety tools that seek reduce this burden. particular, balanced truncation is system-theoretic model which obtains an efficient reduced-dimension by projecting operators onto directions trade off reachability and observability as expressed through associated Gramians. introduce Gramian definitions relevant setting propose based on these Gramians yield reduced can be used cheaply approximate mean covariance. Our exploit natural connections between (i) prior covariance (ii) Fisher information. The resulting then inherits stability properties error bounds theoretic considerations, in some settings yields optimal approximation. Numerical demonstrations two benchmark problems show our method near-optimal approximations with order-of-magnitude reduction.

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ژورنال

عنوان ژورنال: Journal of Scientific Computing

سال: 2022

ISSN: ['1573-7691', '0885-7474']

DOI: https://doi.org/10.1007/s10915-022-01798-8