Would it be possible to implement a Random Walk Covariance Kernel (a.k.a Diffusion Kernel) in the pymc3.gp.cov module? I have inferred a network among my predictors (X) and would like to implement something similar to, e.g. Equation 1 in Urry and Sollich, 2013 for my covariance function (Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs).

This takes the form: Cov_func = a x exp(M x l), where M is a NxN covariance matrix of X (txN), with diagonal elements equal to the negative sum of each respective row: M_{ii} = -1* \sum_{j\neq i} M_{ij}. a and l are then hyperparameters of the covariance function.