  As it is known, universal codes, which estimate the entropy rate
consistently, exist for stationary ergodic sources over finite alphabets but
not over countably infinite ones. We generalize universal coding as the problem
of universal densities with respect to a fixed reference measure on a countably
generated measurable space. We show that universal densities, which estimate
the differential entropy rate consistently, exist for finite reference
measures. Thus finite alphabets are not necessary in some sense. To exhibit a
universal density, we adapt the non-parametric differential (NPD) entropy rate
estimator by Feutrill and Roughan. Our modification is analogous to Ryabko's
modification of prediction by partial matching (PPM) by Cleary and Witten.
Whereas Ryabko considered a mixture over Markov orders, we consider a mixture
over quantization levels. Moreover, we demonstrate that any universal density
induces a strongly consistent Ces\`aro mean estimator of conditional density
given an infinite past. This yields a universal predictor with the $0-1$ loss
for a countable alphabet. Finally, we specialize universal densities to
processes over natural numbers and on the real line. We derive sufficient
conditions for consistent estimation of the entropy rate with respect to
infinite reference measures in these domains.
