  Representations based on random walks can exploit discrete data distributions
for clustering and classification. We extend such representations from discrete
to continuous distributions. Transition probabilities are now calculated using
a diffusion equation with a diffusion coefficient that inversely depends on the
data density. We relate this diffusion equation to a path integral and derive
the corresponding path probability measure. The framework is useful for
incorporating continuous data densities and prior knowledge.
