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Belief propagation (BP) algorithm has been becoming increasingly a popular method for probabilstic inference on general graph-ical 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(More)
In order to clarify acute-phase response in brain, we investigated induction of metallothionein (MT) genes by administrating an endotoxin (lipopolysaccharide) in rat intraperitoneum. We performed in situ hybridization on the serial brain sections to identify the cells expressing the MT genes in acute-phase. After endotoxin administration, transcripts of MT(More)
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(More)
The belief propagation (BP) algorithm is a tool with which one can calculate beliefs, marginal probabilities, of stochastic networks without loops (e.g., Bayesian networks) in a time proportional to the number of nodes. For networks with loops, it may not converge and, even if it converges , beliefs may not equal to exact marginal probabilities although its(More)
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