with respect to log 𝜏. Furthermore, 𝐿 0 (𝝍) and 𝑅 ∞ (𝝍) are assumed to be GPs. This framework is termed quasi-GP DRT (qGP-DRT) as it combines a GP (with respect to 𝝍) with an fGP (with respect to log 𝜏). This model advances EIS data analysis by enabling the simultaneous processing of multiple EIS spectra through probabilistic DRT regression (Figure ). To ensure its consistency, the qGP-DRT model was tested against noise-corrupted EIS synthetic experiments, showing that the deconvolved DRT closely matched analytical results. Furthermore, validation against real experimental data from fuel cells and batteries showed consistent DRT inversion against equivalent circuit models. Importantly, the qGP-DRT framework provides Bayesian probabilistic credible bands of impedance and DRT, allowing the assessment of the uncertainty with respect to both. In short, this work introduces a novel probabilistic approach for EIS data analysis based on Gaussian processes, paving the way for new research avenues in DRT analysis of multiple EIS spectra at a time.