Context models on sequences of covers

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.

• Type: Technical Report
• Year: 2010
• Where: arXiv:1005.2263
• Authors: Christos Dimitrakakis
• Attachments: 1 file(s)
• Keywords: ARXIV:1005.2263 2010, Bayesian Inference, Conditional Density Estimation, Density Estimation, Markov Models, Nonparametric, Online Learning, Partitioning, Prediction