  The edge density and temperature of tokamak plasmas are strongly correlated
with energy and particle confinement and their quantification is fundamental to
understanding edge dynamics. These quantities exhibit behaviours ranging from
sharp plasma gradients and fast transient phenomena (e.g. transitions between
low and high confinement regimes) to nominal stationary phases. Analysis of
experimental edge measurements therefore require robust fitting techniques to
capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma
diagnostics inevitably involve measurement errors and data analysis requires a
statistical framework to accurately quantify uncertainties. This paper outlines
a generalized multidimensional adaptive Gaussian process routine capable of
automatically handling noisy data and spatiotemporal correlations. We focus on
the edge-pedestal region in order to underline advancements in quantifying
time-dependent plasma profiles including transport barrier formation on the
Alcator C-Mod tokamak.
