Learning Weighted Model Integration Distributions


  • Paolo Morettin University of Trento
  • Samuel Kolb KU Leuven
  • Stefano Teso University of Trento
  • Andrea Passerini University of Trento




Weighted model integration (WMI) is a framework for probabilistic inference over distributions with discrete and continuous variables and structured supports. Despite the growing popularity of WMI, existing density estimators ignore the problem of learning a structured support, and thus fail to handle unfeasible configurations and piecewise-linear relations between continuous variables. We propose lariat, a novel method to tackle this challenging problem. In a first step, our approach induces an SMT(ℒℛA) formula representing the support of the structured distribution. Next, it combines the latter with a density learned using a state-of-the-art estimation method. The overall model automatically accounts for the discontinuous nature of the underlying structured distribution. Our experimental results with synthetic and real-world data highlight the promise of the approach.




How to Cite

Morettin, P., Kolb, S., Teso, S., & Passerini, A. (2020). Learning Weighted Model Integration Distributions. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5224-5231. https://doi.org/10.1609/aaai.v34i04.5967



AAAI Technical Track: Machine Learning