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Revision n. 1
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If you have continuous variables with a roughly Gaussian distribution, I have found that Gaussian graphical models give a good framework. In particular, recent work on sparse inverse covariance estimation gives good algorithmic tools.
For this theoretical setting, the book "Graphical Models", by S. Lauritzen (1996) is a really good read. It is quite theoretical, but well-written.
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Revision n. 2
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If you have continuous variables with a roughly Gaussian distribution, I have found that Gaussian graphical models give a good framework. In particular, recent work on sparse inverse covariance estimation gives good algorithmic tools.
For this theoretical setting, the book "Graphical Models", by S. Lauritzen (1996) is a really good read. It is quite theoretical, but well-written.
I realize that the question was for a 'student', so I should add that this is probably not the book to start with, and I would start with scanning through the Jordan book mentioned above.
