|Stochastic Systems Group|
Erik Sudderth and Alex Ihler
SSG Doctoral Students
Graphical models provide a powerful general framework for formulating and solving problems of statistical inference and machine learning. In many applications of graphical models, such as those arising in computer vision, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. However, due to the limitations of existing inference algorithms, it is often necessary to form coarse, discrete approximations to such models.
In this talk, we describe a nonparametric belief propagation (NBP) algorithm, which uses stochastic methods to propagate kernel-based approximations to the true continuous messages. Each NBP message update is based on an efficient sampling procedure which can accomodate an extremely broad class of potential functions, allowing easy adaptation to new application areas. We validate our method using comparisons to continuous BP for Gaussian networks, and an application to the stereo vision problem.
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