Schlandals is a state-of-the-art Projected Weighted Model Counter specialized for probabilistic inference over discrete probability distributions. Currently, there are known modelization for the following problems

• Computing the marginal probabilities of a variable in a Bayesian Network
• Computing the probability that two nodes are connected in a probabilistic graph
• Computing the probability of ProbLog programs

For more information on how to use Schlandals and its mechanics, check the documentation (still in construction). You can cite Schlandals using the following bibtex entry

@InProceedings{schlandals
  author =	{Dubray, Alexandre and Schaus, Pierre and Nijssen, Siegfried},
  title =	{{Probabilistic Inference by Projected Weighted Model Counting on Horn Clauses}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  year =	{2023},
  doi =		{10.4230/LIPIcs.CP.2023.15},
}

If you use our LDS-based approximation, you can also cite

@InProceedings{schlandals_anytime_approximation
  author =	{Dubray, Alexandre and Schaus, Pierre and Nijssen, Siegfried},
  title =	{{Anytime Weighted Model Counting With Approximation Guarantees For Probabilistic Inference}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  year =	{2024},
}