Probability-based systems, on the other hand, capture the expert's qualitative physical understanding of the system under diagnosis and use this knowledge to construct diagnostic models. While it is possible to capture diagnostic information directly in a probabilistic system, our experience shows that it is much faster and easier to assess causal models. Another reason is that Bayesian networks are a fundamentally more modular representation of uncertain knowledge than rule-based systems. This makes them easier to maintain, and to adapt them to different contexts, than there rule-based relatives. The uncertain decision making context, and the modularity and ease of maintenance, make the Bayesian networks quite attractive.
Bayesian networks are very intuitive for nonexperts.
We had a United Airlines engine person drawing and debugging these models within a few hours of exposure to them. The NASA Vista people needed to have a way to fuse together the implications of multiple sensors, some noisy, when we began the Vista project. There was no good way to take into consideration all of the sensors and their failure modes with a fault tree. I'm all in the question and the positive responses, I'm just being flippant and trying to inject a different perspective. Bayesian nets in the narrow sense are the kind of graph plus probability tables that have dominated UAI conferences in previous years.