HUGIN API Reference Manual. The “HUGIN API 7.8 Reference Manual” provides a reference for the C language Application Program Interface to the HUGIN system. However, brief descriptions of the Java and C++ versions are also provided (see Chapter 1). The present manual assumes familiarity with the methodology of Bayesian belief networks and (limited memory) influence diagrams (LIMIDs) as well as knowledge of the C programming language and programming concepts.

References in zbMATH (referenced in 15 articles )

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  1. Butz, Cory J.; Oliveira, Jhonatan S.; Madsen, Anders L.: Bayesian network inference using marginal trees (2016)
  2. Graversen, Therese; Lauritzen, Steffen: Computational aspects of DNA mixture analysis (2015)
  3. Cowell, Robert G.; Smith, James Q.: Causal discovery through MAP selection of stratified chain event graphs (2014)
  4. Madsen, Anders L.; Butz, Cory J.: Ordering arc-reversal operations when eliminating variables in lazy AR propagation (2013) ioport
  5. Ottosen, Thorsten J.; Vomlel, Jiří: All roads lead to Rome -- new search methods for the optimal triangulation problem (2012)
  6. Harrington, Anthony; Cahill, Vinny: Model-driven engineering of planning and optimisation algorithms for pervasive computing environments (2011) ioport
  7. Jensen, Finn V.; Nielsen, Thomas Dyhre: Probabilistic decision graphs for optimization under uncertainty (2011)
  8. Madsen, A.L.: Improvements to message computation in lazy propagation (2010) ioport
  9. Søndberg-Jeppesen, Nicolaj; Jensen, Finn V.: A PGM framework for recursive modeling of players in simple sequential Bayesian games (2010) ioport
  10. Matías, J.M.; Rivas, T.; Martín, J.E.; Taboada, J.: A machine learning methodology for the analysis of workplace accidents (2008)
  11. Pourret, Oliver (ed.); Naïm, Patrick (ed.); Marcot, Bruce (ed.): Bayesian networks. A practical guide to applications. (2008)
  12. Johnson, Pontus; Lagerström, Robert; Närman, Per; Simonsson, Mårten: Enterprise architecture analysis with extended influence diagrams (2007) ioport
  13. Mengshoel, Ole J.; Wilkins, David C.; Roth, Dan: Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering (2006)
  14. Lauritzen, Steffen L.; Sheehan, Nuala A.: Graphical models for genetic analyses (2003)
  15. Gökçay, Korhan; Bilgiç, Taner: Troubleshooting using probabilistic networks and value of information (2002)