Banjo: Bayesian Network Inference with Java Objects. Banjo is a software application and framework for structure learning of static and dynamic Bayesian networks, developed under the direction of Alexander J. Hartemink in the Department of Computer Science at Duke University. Banjo was designed from the ground up to provide efficient structure inference when analyzing large, research-oriented data sets, while at the same time being accessible enough for students and researchers to explore and experiment with the algorithms. Because it is implemented in Java, the framework is easy to maintain and extend.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Karwa, Vishesh; Slavković, Aleksandra B.; Donnell, Eric T.: Causal inference in transportation safety studies: comparison of potential outcomes and causal diagrams (2011)
- Bevilacqua, V.; Mastronardi, G.; Menolascina, F.; Pannarale, P.; Romanazzi, G.: Bayesian gene regulatory network inference optimization by means of genetic algorithms (2009)