HUGIN

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 18 articles )

Showing results 1 to 18 of 18.
Sorted by year (citations)

  1. Butz, Cory J.; Oliveira, Jhonatan S.; dos Santos, André E.; Madsen, Anders L.: An empirical study of Bayesian network inference with simple propagation (2018)
  2. Datta, Sagnik; Gayraud, Ghislaine; Leclerc, Eric; Bois, Frederic Y.: \textitGraph_sampler: a simple tool for fully Bayesian analyses of DAG-models (2017)
  3. Butz, Cory J.; Oliveira, Jhonatan S.; Madsen, Anders L.: Bayesian network inference using marginal trees (2016)
  4. Graversen, Therese; Lauritzen, Steffen: Computational aspects of DNA mixture analysis (2015)
  5. Cowell, Robert G.; Smith, James Q.: Causal discovery through MAP selection of stratified chain event graphs (2014)
  6. Madsen, Anders L.; Butz, Cory J.: Ordering arc-reversal operations when eliminating variables in lazy AR propagation (2013) ioport
  7. Ottosen, Thorsten J.; Vomlel, Jiří: All roads lead to Rome -- new search methods for the optimal triangulation problem (2012)
  8. Søren Højsgaard: Graphical Independence Networks with the gRain Package for R (2012) not zbMATH
  9. Harrington, Anthony; Cahill, Vinny: Model-driven engineering of planning and optimisation algorithms for pervasive computing environments (2011) ioport
  10. Jensen, Finn V.; Nielsen, Thomas Dyhre: Probabilistic decision graphs for optimization under uncertainty (2011)
  11. Madsen, A. L.: Improvements to message computation in lazy propagation (2010) ioport
  12. Søndberg-Jeppesen, Nicolaj; Jensen, Finn V.: A PGM framework for recursive modeling of players in simple sequential Bayesian games (2010) ioport
  13. Matías, J. M.; Rivas, T.; Martín, J. E.; Taboada, J.: A machine learning methodology for the analysis of workplace accidents (2008)
  14. Pourret, Oliver (ed.); Naïm, Patrick (ed.); Marcot, Bruce (ed.): Bayesian networks. A practical guide to applications. (2008)
  15. Johnson, Pontus; Lagerström, Robert; Närman, Per; Simonsson, Mårten: Enterprise architecture analysis with extended influence diagrams (2007) ioport
  16. 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)
  17. Lauritzen, Steffen L.; Sheehan, Nuala A.: Graphical models for genetic analyses (2003)
  18. Gökçay, Korhan; Bilgiç, Taner: Troubleshooting using probabilistic networks and value of information (2002)