Hi Brandon,
I’m not sure what you mean by “generalized joint distribution”. Let me take a stab and see if I’m in the ballpark: When I want a joint distribution p(a,b), I’ll just represent it in the Foundry using the chain rule: p(a|b)*p(b)
Data-structure-wise, I usually do this with the prior (p(b)) as the appropriate distribution, a DataDistribution or UnivariateGaussian etc. and then the conditionals (p(a|b)) in a HashMap for discrete b or some type of other lookup for continuous b.
Does that make sense?
Let me know if I can help!
Kevin
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Kevin R. Dixon
Sandia National Laboratories
Critical Systems Security (05621)
MS0672, TA-I: 729/134
tel: (505) 284-5615
fax: (505) 284-3258
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