Statistical modeling of frequency responses using linear Bayesian vector fitting

This article presents a Bayesian extension of the vector fitting (VF) procedure for rational approximation of frequency-domain responses. The proposed method treats the linear part of VF in a Bayesian way, while propagating distributions through the nonlinear part by sampling. As such, it is capable of providing data-driven uncertainty information along with the rational fit. The Bayesian VF technique is applied to two realistic design examples, a double folded stub filter and a RAM memory channel, demonstrating its validity and highlighting three potential applications of this novel framework.

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