Exploiting Data-Independence for Fast Belief-Propagation

Maximum a posteriori (MAP) inference in graphical models requires that we maximize the sum of two terms: a data-dependent term, encoding the conditional likelihood of a certain labeling given an observation, and a data-independent term, encoding some prior on labelings. Often, data-dependent factors contain fewer latent variables than dataindependent factors

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Julian John McAuley, Tibério S. Caetano
• Keywords: Data-dependent Term, Data-independent Terms, Fewer Latent Variables, ICML 2010, Machine Learning