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I am exploring using Bayesian Networks to identify the best parameters within a system design, to improve its performance. I'm trying to find any case studies where this has been used successfully or unsuccessfully.

As an example consider an automatic assisted anti-collision system on a car. It could use some combination of ultrasonic, radar & IR seekers to sense and measure the closing speed with the car in front. These all have different costs / reliabilities and perform differently in different weather conditions. I am interested in understanding how effective Bayesian Networks can be in evaluating the trade space and identifying the best solutions to these types of problems.

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As I can see you're searching for a case study. Learning from data is useful because you can learn both the conditional probability distributions (CPDs) and the structure of the network. The best resource I've read is a freely-available Microsoft tech report.

Have a look at the paper, maybe it can help you upto some extend. I'll suggest you to start with Chapter 7 of it.

Hope it helps. Cheers!

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There is a paper that was published at Systems&Cybernetics journal.

See how they use Bayesian Networks with sensor fusion:

Active and dynamic information fusion for multi-sensor systems with dynamic bayesian networks.

Hope it helps.

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