Loopy Belief Propogation and Gibbs Measures

Loopy Belief Propagation and Gibbs Measures

Loopy Belief Propagation Algorithm

check the effectiveness. In some cases, such as Dobrushin's condition is satisfied, the error bounds and the improvement procedure seem effective. Nevertheless, the region where one can obtain good bounds seems restrictive. We give some remarks in the rest of this section. First, the concept of estimates we used in this paper was developed for general Gibbs measures, so that there may be a poss...

Error Bounds Between Marginal Probabilities and Beliefs of Loopy Belief Propagation Algorithm

Belief propagation (BP) algorithm has been becoming increasingly a popular method for probabilstic inference on general graphical models. When networks have loops, it may not converge and, even if converges, beliefs, Le., the result of the algorithm, may not be equal to exact marginal probabilties. When networks have loops, the algorithm is called Loopy BP (LBP). Tatikonda and Jordan applied Gi...

Approximate Inference by Compilation to Arithmetic Circuits

Arithmetic circuits (ACs) exploit context-specific independence and determinism to allow exact inference even in networks with high treewidth. In this paper, we introduce the first ever approximate inference methods using ACs, for domains where exact inference remains intractable. We propose and evaluate a variety of techniques based on exact compilation, forward sampling, AC structure learning...

Collective Classification Techniques: An Experimental Study

Collective classification is the area in machine learning, in which unknown nodes in the network are classified based on the classes assigned to the known nodes and the network structure only. Three collective classification algorithms were described and examined in the paper: Iterative Classification (ICA), Gibbs Sampling (GS) and Loopy Belief Propagation (LBP). Experiments on various networks...