  We study expressivity of Markov logic networks (MLNs). We introduce complex
MLNs, which use complex-valued weights, and we show that, unlike standard MLNs
with real-valued weights, complex MLNs are fully expressive. We then observe
that discrete Fourier transform can be computed using weighted first order
model counting (WFOMC) with complex weights and use this observation to design
an algorithm for computing relational marginal polytopes which needs
substantially less calls to a WFOMC oracle than a recent algorithm.
