  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
