A Novel Tree Block-Coordinate Method for MAP Inference
Christopher Zach · Lecture notes in computer science · 2015
Block-coordinate methods inspired by belief propagation are among the most successful methods for approximate MAP inference in graphical models. The set of unknowns optimally updated in such block-coordinate methods is typically very small and spans only single edges or shallow trees. We derive a method that optimally updates sets of unknowns spanned by an arbitrary tree that is different from one reported in the literature. It provides some insight why “tree block-coordinate” methods are not as useful as expected, and enables a simple technique to makes these tree updates more effective. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.