Domain size asymptotics for Markov logic networks

arXiv:2509.04192v2 Announce Type: replace Abstract: A Markov logic network (MLN) M determines a probability distribution P_n^M on the set mathbf{W}_n of structures, or ``possible worlds'', with domain {1, ldots, n}. We study the properties of such distributions as n tends to infinity. We show that with mild assumptions on an MLN M with one soft constraint with an arbitrary positive weight the distribution P_n^M will behave quite differently from the uniform distribution P_n^{uni} on mathbf{W}_n for all sufficiently large n. For a language with only one relation symbol R which has arity 1 we give an almost complete characterization of the possible asymptotic behaviours of P_n^M as n o infty, where M may be any MLN for this language. The asymptotic behaviour depends on the soft constraints and weights of the MLN. This characterization is used to show that if the language under consideration contains at least one relation symbol of arity 1 then the following holds: (a) There is an MLN M such that for every lifted Bayesian network (LBN) G there are infinitely many n such that M and G determine different distributions on mathbf{W}_n. (b) There is an LBN G such that for every MLN M there are infinitely many n such that G and M determine different distributions on mathbf{W}_n. We also show that, in the limit, the weight dimension and the domain size dimension may behave completely differently.

Source: arXiv cs.AI | 2026-05-28