In a hierarchical Bayesian framework, 𝜆 is a log 𝜏-dependent random variable . Thus, the implementation needs a random vector 𝝀 ∈ ℝ 𝑁 𝜆 with 𝑁 𝜆 defined as 𝑁 𝜆 = 𝑀 -𝑞 where 𝑀 is the number of EIS frequencies and 𝑞 is the derivative order of the differentiation matrix 𝑳 in (2) .