Layout

Context

Machine-learning Bayesian networks can produce very complex structures, which can be difficult to comprehend visually.
Many of these challenges can be reduced by properly arranging the nodes in the newly discovered network structure. However, except in trivial cases, this can’t realistically be done manually.
BayesiaLab offers numerous layout algorithms that can automate the “untangling” of crisscrossing nodes and arcs and quickly produce a comprehensible graph.