In the Bayesian framework, entropy maximization serves as a prior that favors smoother, more distributed solutions . This approach, implemented through (3), constrains the solution space by promoting simpler (more entropic) DRT spectra, thereby reducing noise amplification and preventing the formation of artificial peaks, favoring distributed time constants rather than discrete relaxation times. While entropy regularization has successfully addressed various ill-posed problems , its application to electrochemical DRT analysis remains largely unexplored, particularly within a Bayesian regularized regression framework.