Netica

Netica is a powerful, easy-to-use, complete program for working with belief networks and influence diagrams. It has an intuitive and smooth user interface for drawing the networks, and the relationships between variables may be entered as individual probabilities, in the form of equations, or learned from data files (which may be in ordinary tab-delimited form and have ”missing data”). ...


References in zbMATH (referenced in 14 articles )

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  1. Tomas Beuzen: pybeach: A Python package for extracting the locationof dune toes on beach profile transects (2019) not zbMATH
  2. Yet, Barbaros; Neil, Martin; Fenton, Norman; Constantinou, Anthony; Dementiev, Eugene: An improved method for solving hybrid influence diagrams (2018)
  3. Abbal, Philippe; Sablayrolles, Jean-Marie; Matzner-Lober, Éric; Boursiquot, Jean-Michel; Baudrit, Cedric; Carbonneau, Alain: A decision support system for vine growers based on a Bayesian network (2016)
  4. Gheisari, S.; Meybodi, M. R.: BNC-PSO: structure learning of Bayesian networks by particle swarm optimization (2016)
  5. Levy, Roy; Mislevy, Robert J.: Bayesian psychometric modeling (2016)
  6. Donald, Margaret R.; Mengersen, Kerrie L.: Methods for constructing uncertainty intervals for queries of Bayesian nets (2014)
  7. Fang, Zhipeng; Yue, Kun; Zhang, Jixian; Zhang, Dehai; Liu, Weiyi: Predicting click-through rates of new advertisements based on the Bayesian network (2014)
  8. Elzer, Stephanie; Carberry, Sandra; Zukerman, Ingrid: The automated understanding of simple bar charts (2011) ioport
  9. Ismail, Mohamed A.; Sadiq, Rehan; Soleymani, Hamid R.; Tesfamariam, Solomon: Developing a road performance index using a Bayesian belief network model (2011) ioport
  10. Jensen, Finn V.; Nielsen, Thomas Dyhre: Probabilistic decision graphs for optimization under uncertainty (2011)
  11. Liu, Wei-Yi; Yue, Kun; Gao, Ming-Hai: Constructing probabilistic graphical model from predicate formulas for fusing logical and probabilistic knowledge (2011) ioport
  12. Butz, C. J.; Hua, S.; Konkel, K.; Yao, H.: Join tree propagation with prioritized messages (2010)
  13. Pourret, Oliver (ed.); Naïm, Patrick (ed.); Marcot, Bruce (ed.): Bayesian networks. A practical guide to applications. (2008)
  14. Yap, Ghim-Eng; Tan, Ah-Hwee; Pang, Hwee-Hwa: Explaining inferences in Bayesian networks (2007) ioport