Ergo: A Graphical Environment for Constructing Bayesian
Artificial Intelligence
2013-04-05 v1
Abstract
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
Keywords
Cite
@article{arxiv.1304.1095,
title = {Ergo: A Graphical Environment for Constructing Bayesian},
author = {Ingo Beinlich and Edward H. Herskovits},
journal= {arXiv preprint arXiv:1304.1095},
year = {2013}
}
Comments
Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)