Deconvolving 𝛾(log 𝜏 , 𝝍) from EIS data is challenging, as it requires solving an ill-posed inverse problem . Regularized regression , deep neural networks , and other methods have been developed for this purpose. Our group introduced a probabilistic approach for DRT deconvolution, reformulating regularized regression in a Bayesian context ,