Inference in Probabilistic Graphical Models by Graph Neural Networks

  title={Inference in Probabilistic Graphical Models by Graph Neural Networks},
  author={KiJung Yoon and Renjie Liao and Yuwen Xiong and Lisa Zhang and Ethan Fetaya and Raquel Urtasun and Richard S. Zemel and Xaq Pitkow},
A fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. Messagepassing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these… CONTINUE READING
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