LibDAI

LibDAI: a free and open source C++ library for discrete approximate inference in graphical models This paper describes the software package libDAI, a free & open source C++ library that provides implementations of various exact and approximate inference methods for graphical models with discrete-valued variables. libDAI supports directed graphical models (Bayesian networks) as well as undirected ones (Markov random fields and factor graphs). It offers various approximations of the partition sum, marginal probability distributions and maximum probability states. Parameter learning is also supported. A feature comparison with other open source software packages for approximate inference is given. libDAI is licensed under the GPL v2+ license and is available at http://www.libdai.org.


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Hanheide, Marc; Göbelbecker, Moritz; Horn, Graham S.; Pronobis, Andrzej; Sjöö, Kristoffer; Aydemir, Alper; Jensfelt, Patric; Gretton, Charles; Dearden, Richard; Janicek, Miroslav; Zender, Hendrik; Kruijff, Geert-Jan; Hawes, Nick; Wyatt, Jeremy L.: Robot task planning and explanation in open and uncertain worlds (2017)
  2. Anderson, Eric C.; Ng, Thomas C.: Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation (2016)
  3. Kappes, Jörg Hendrik; Swoboda, Paul; Savchynskyy, Bogdan; Hazan, Tamir; Schnörr, Christoph: Multicuts and perturb & MAP for probabilistic graph clustering (2016)
  4. Al-Dujaili, Abdullah; Merciol, François; Lefèvre, Sébastien: GraphBPT: an efficient hierarchical data structure for image representation and probabilistic inference (2015)
  5. Antonucci, Alessandro; de Campos, Cassio P.; Huber, David; Zaffalon, Marco: Approximate credal network updating by linear programming with applications to decision making (2015)
  6. Lowd, Daniel; Rooshenas, Amirmohammad: The Libra toolkit for probabilistic models (2015)
  7. Ravanbakhsh, Siamak; Greiner, Russell: Perturbed message passing for constraint satisfaction problems (2015)
  8. Müller, Andreas C.; Behnke, Sven: Pystruct-learning structured prediction in Python (2014)
  9. Kappen, Hilbert J.; Gómez, Vicenç; Opper, Manfred: Optimal control as a graphical model inference problem (2012)
  10. Mooij, Joris M.: LibDAI: a free and open source C++ library for discrete approximate inference in graphical models (2010)