MURI Research
Topic: Nonparametric Inference for Graphical Models
By: Erik Sudderth (MIT), Alexander Ihler (MIT), William Freeman (MIT), Alan Willsky (MIT)
In many applications of graphical models, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. There exist inference algorithms for discrete approximations to these continuous distributions, but for the high-dimensional variables arising in domains such as visual tracking, discrete inference becomes infeasible. Drawing on ideas from regularized particle filters and belief propagation (BP), we have developed a nonparametric belief propagation (NBP) algorithm applicable to general graphs. Each NBP iteration uses an efficient sampling procedure to update kernel-based approximations to the true, continuous likelihoods. We have applied NBP to a pair of challenging applications: distributed self-calibration of an ad hoc sensor network from range measurements, and visual tracking of three-dimensional hand motion using a kinematic model. Current research focuses on the continuing development of efficient sampling and model identification algorithms, and exploration of additional application domains.

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