Low rank continuous-space graphical models
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1064-1072, 2012.
Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven difficult to find general constructions for models in which efficient exact inference is possible, outside of the classical cases of models with restricted graph structure (chain, tree, etc.) and linear-Gaussian or discrete potentials. In this work we identify a tree graphical model class in which exact inference can be performed efficiently, owing to a certain “low-rank” structure in the potentials. We explore this new class of models by applying the resulting inference methods to neural spike rate estimation and motion-capture joint-angle smoothing tasks.