Introduction

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{dubray_et_al:LIPIcs.CP.2023.15,
  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)},
  pages =	{15:1--15:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/19052},
  URN =		{urn:nbn:de:0030-drops-190520},
  doi =		{10.4230/LIPIcs.CP.2023.15},
  annote =	{Keywords: Model Counting, Bayesian Networks, Probabilistic Networks}
}