  The flavor structure of the Standard Model might arise from random selection
on a landscape. We propose a class of simple models, ``Gaussian landscapes,''
where Yukawa couplings derive from overlap integrals of Gaussian wavefunctions
on extra-dimensions. Statistics of vacua are generated by scanning the peak
positions of these zero-modes, giving probability distributions for all flavor
observables. Gaussian landscapes can broadly account for all observed flavor
patterns with very few free parameters. For example, the generation structure
in the quark sector follows from the overlap integrals for both the up and down
type Yukawas sharing the localized wavefunctions of the quark doublets and the
Higgs boson. Although Gaussian landscapes predict broad probability
distributions, the flavor observables are correlated and we show that
accounting for measured flavor parameters creates sharper distributions for
future neutrino measurements.
