GeNie Modeler is a decision modeling environment implementing influence diagrams and Bayesian networks, developed at the Decision Systems Laboratory, University of Pittsburgh, and licensed since 2015 to BayesFusion, LLC. It has an intuitive graphical interface that includes hierarchical sub models, Windows-style tree view, and a comprehensive HTML-based on-line help that includes beginners-oriented tutorials for Bayesian networks, influence diagrams, and basic decision analytic techniques. GeNie Modeler implements multi-attribute utility functions, Noisy-OR and Noisy-AND gates, value of information, and sensitivity analysis. In addition to its native file format, GeNie supports reading and writing of Hugin, Netica, and Ergo files and can be used for conversion of models among these programs. GeNIe Modeler comes with SMILE Engine (Structural Modeling, Inference, and Learning Engine) an application programmer’s interface (API) library of C++ classes that allows object-oriented software development based on decision-analytic techniques. Wrappers for SMILE Engine are also available and allow it to be used from Java, .NET environment, or as an ActiveX control. GeNIe Modeler and SMILE Engine are available free of charge to academic users and can be downloaded from BayesFusion, LLC’s academic web site. The package includes several large Bayesian network and influence diagrams models, useful in teaching. GeNIe Modeler implementation of all influence diagrams from the textbook Making Hard Decisions by Robert T. Clemen can be found on the Model Repository page.

References in zbMATH (referenced in 7 articles )

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  1. Cinicioglu, Esma Nur; Yenilmez, Taylan: Determination of variables for a Bayesian network and the most precious one (2016)
  2. Greco, Salvatore (ed.); Ehrgott, Matthias (ed.); Figueira, José Rui (ed.): Multiple criteria decision analysis. State of the art surveys. In 2 volumes (2016)
  3. Cene, E.; Karaman, F.: Analysing organic food buyers’ perceptions with Bayesian networks: a case study in Turkey (2015)
  4. Jr., Estevam R. Hruschka; Nicoletti, Maria Do Carmo: Roles played by Bayesian networks in machine learning: an empirical investigation (2013) ioport
  5. Jensen, Finn V.; Nielsen, Thomas Dyhre: Probabilistic decision graphs for optimization under uncertainty (2011)
  6. Blecic, Ivan; Cecchini, Arnaldo; Trunfio, Giuseppe A.: A decision support tool coupling a causal model and a multi-objective genetic algorithm (2007) ioport
  7. Hruschka jun., Estevam R.; Hruschka, Eduardo R.; Ebecken, Nelson F. F.: Bayesian networks for imputation in classification problems (2007) ioport