Ergo: A Graphical Environment for Constructing Bayesian
This work addresses the problem of complex Bayesian network construction for researchers and practitioners, but it appears incremental as it builds on existing algorithms and paradigms.
The authors tackled the challenge of simplifying the creation of Bayesian belief networks by developing Ergo, a graphical environment that provides clarity and high performance on inexpensive hardware, though no concrete numbers are provided.
We describe an environment that considerably simplifies the process of generating Bayesian belief networks. The system has been implemented on readily available, inexpensive hardware, and provides clarity and high performance. We present an introduction to Bayesian belief networks, discuss algorithms for inference with these networks, and delineate the classes of problems that can be solved with this paradigm. We then describe the hardware and software that constitute the system, and illustrate Ergo's use with several example