Inference in Probabilistic Graphical Models by Graph Neural Networks

Understand

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.
• Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops.
• Here we use Graph Neural Networks (GNNs) to learn a message-passing algorithm that solves these inference tasks.