The problem that we have is as follows. We have close to 60 discrete random variables each of which shall take on an average of 5 categorical values. We have developed a Bayesian network representation using our domain knowledge. We have the data for these 50 discrete random variables and how they interplay with each other using some logs.
We are not able to compile/infer the conditional probability distribution and marginal probability distribution for this Bayesian network from the data/spreadsheet. The software libraries (gRain, bnlearn in CRAN) give up.
As of now, we are trying to solve the problem by introducing some latent variables and by exploiting some local structure inherent in the problem. We are not successful so far. Any generic suggestions in terms of algorithms, tools to model, infer and solve problems of this scale shall be very useful.