Maximum a Posteriori Inference #2
Description
Implement algorithms for maximum a posteriori inference, that is, finding the probable interpretation of a selected set of facts. Implement at least a brute-force algorithm and an algorithm that calls clingo or plingo for optimization/sampling. Can start with simple cases (acyclic/stratified programs) then move to programs with cycles and max-ent semantics.