I am wondering why some methods transform the underlying graphical model (Bayesian Network for example) to a junction tree? What are the advantages?  Also what are the limitations?  I believe it's for computational purposes. If that's the case under what circumstances it is not recommended to transform the underlying DAG to joint-trees? Edit: speaking about graphical models in general (whether they are probabilistic or not), is there some guidelines when to transform them i.e. decompose them? 1 Junction trees are also known as tree decomposition.
Why use joint trees in Graphical Models?
machine learning