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I learned how to use libpgm in general for Bayesian inference and learning, but I do not understand if I can use it for learning with hidden variable. More precisely, I am trying to implement approach for Social Network Analysing from this paper: Modeling Relationship Strength in Online Social Networks. They suggest to use following architecture

Here

- S(ij) represents vector of similarity between user i and j -
**Observed** - z(ij) is a hidden variable - relationship strength (Normal distribution regularised by
**W**- weights and similarity vector)-**Hidden** - yt(ij) is user interaction(1,2…n -> certain type of interaction e.g. 1=I retweeted j) (function of z and a that involves
**Theta**parameter) -**Observed** - at(ij) is auxiliary variable which represents how often certain interaction occurs -
**Observed**

Approach described in paper for training is quite difficult and involves coding of ascent optimisation. I wonder If I can use libpgm to learn **W** and **Theta** parameters. If yes, how to do it? If no, what libraries I can use to do it.