Loopy Belief inference is only implemented for a single node.

[tawheeler]

I introduced a new inference API:

abstract InferenceMethod

"""
Infer p(query|evidence)
 - inference on a DiscreteBayesNet will always return a DataFrame factor over the evidence variables
"""
infer(im::InferenceMethod, bn::BayesNet, query::Vector{NodeName}; evidence::Assignment=Assignment())

Likelihood and exact inference work perfectly under this.
Loopy belief, however, only takes a query::NodeName, when a Vector{NodeName} would be ideal.

[tawheeler]

@hamzaelsaawy - if you could address this that'd be excellent.

[hamzaelsaawy]

With regards to this and #49, I have a new Factor type, and an inference type.
I'm still testing/integrating though

I've been struggling with this; I don't think LBP supports multiple queries, since it just returns the marginal of any one node (given the evidence) by message passing, not the joint of multiple nodes. I think ...

[mykelk]

If you have multiple query variables, you can turn them into a vector variable, and then proceed with the LBP algorithm.

[henripal]

agreed that LBP won't return the joint but I think that one would expect a LBP call with multiple query nodes to return the marginals for each node, which you already have after the LBP loop terminates?

[tawheeler]

We could just take the factor product over all resulting marginal factors that correspond to query variables.