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0 Kudos

Dynamic Bayesian Networks

In JMP / SAS lacks a whole part dedicated to both dynamic and static Bayesian networks. The importance of Bayesian networks and therefore of the world of machine learning is increasingly important and having the possibility of dedicated commands or packages would be very important. In R-project there is the bnlearn or bnstruct package that allow you to analyze data with repeated temporal dynamics, but in JMP and / or SAS nothing exists. In SAS there is a proc hpbnet procedure, but it generates networks with a target variable and not a network where you don't want to target, but free from constraints, as the two packages bnlearn and / or bnstruct do.

Level II

It can express complex phenomena. Can be expressed probabilistically. These expressions fit the human senses. I also strongly hope the Bayesian Network.

Level III

A data science team at HP has started using BNA for predictor or factor screening for manufacturing problems. It's the first time someone has shown me a canned analysis available to engineers here where I had to say "JMP can't do that".