  We present a class of models that, via a simple construction, enables exact,
incremental, non-parametric, polynomial-time, Bayesian inference of conditional
measures. The approach relies upon creating a sequence of covers on the
conditioning variable and maintaining a different model for each set within a
cover. Inference remains tractable by specifying the probabilistic model in
terms of a random walk within the sequence of covers. We demonstrate the
approach on problems of conditional density estimation, which, to our knowledge
is the first closed-form, non-parametric Bayesian approach to this problem.

Machine Learning;Machine Learning