Deconvolving 𝛾(log 𝜏) from EIS measurements is challenging because an ill-posed inverse problem needs to be solved . Several approaches, including maximum entropy, genetic algorithms, ridge regression, evolutionary algorithms, elastic net, Ridge and Lasso regression, and Bayesian methods have been used . Among them, probabilistic methods have emerged as the method of choice for DRT deconvolution due to their ability to quantify uncertainty. In that context, our research group recently developed the Gaussian process distribution of relaxation times (GP-DRT) method, which assumes the DRT to be a Gaussian process (GP) . The GP-DRT was shown to effectively deconvolve the DRT, fitting both synthetic and real EIS data. One particularly appealing trait of the GP-DRT method is that its hyperparameters can be obtained by maximizing the evidence, ensuring maximal consistency with experiments. The GP-DRT method has, however, two limitations. First, the 𝛾(log 𝜏) estimated using the GP-DRT is unconstrained and, therefore, can be negative (which is unphysical). Second, the current implementation assumes that only the imaginary part of the impedance spectrum can be used . To overcome these limitations, we developed a new model based on a finite approximation of GPs . Herein, this method will be called the finite Gaussian process distribution of relaxation times (fGP-DRT). The fGP-DRT was set up so that it can recover both the real and imaginary parts of the impedance under the constraint that the DRT is non negative, i.e., 𝛾(log 𝜏) ≥ 0. The fGP-DRT method was successfully validated against artificial and real EIS data. Furthermore, like the GP-DRT, the fGP-DRT was used as a machine learning toolbox, allowing the prediction of EIS spectra at unmeasured frequencies. This work extends the interpretation of EIS data through the lens of probabilistic modeling, and will likely inspire more research works that leverage GPs for DRT deconvolution.