Context models on sequences of covers

13 years 3 months ago
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.
Christos Dimitrakakis
Added 06 Sep 2010
Updated 20 Mar 2012
Type Technical Report
Year 2010
Where arXiv:1005.2263
Authors Christos Dimitrakakis
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