The generalised distribution semantics and projective families of distributions
This provides a theoretical unification for probabilistic modeling frameworks, though it appears incremental in extending existing semantics.
The authors generalized the distribution semantics from probabilistic logic programming to unify frameworks like probabilistic databases and discrete lifted Bayesian networks, then characterized which projective families of distributions can be represented in this generalization, showing both limitations and that simple logic programs suffice for representable cases.
We generalise the distribution semantics underpinning probabilistic logic programming by distilling its essential concept, the separation of a free random component and a deterministic part. This abstracts the core ideas beyond logic programming as such to encompass frameworks from probabilistic databases, probabilistic finite model theory and discrete lifted Bayesian networks. To demonstrate the usefulness of such a general approach, we completely characterise the projective families of distributions representable in the generalised distribution semantics and we demonstrate both that large classes of interesting projective families cannot be represented in a generalised distribution semantics and that already a very limited fragment of logic programming (acyclic determinate logic programs) in the determinsitic part suffices to represent all those projective families that are representable in the generalised distribution semantics at all.