Is there any domain where Bayesian Networks outperform neural networks? Neural networks get top results in Computer Vision tasks (see MNIST, ILSVRC, Kaggle Galaxy Challenge).  They seem to outperform every other approach in Computer Vision. But there are also other tasks:


Kaggle Molecular Activity Challenge
Regression: Kaggle Rain prediction, also the 2nd place
Grasp and Lift 2nd also third place - Identify hand motions from EEG recordings


I'm not too sure about ASR (automatic speech recognition) and machine translation, but I think I've also heard that (recurrent) neural networks (start to) outperform other approaches.

I am currently learning about Bayesian Networks and I wonder in which cases those models are usually applied. So my question is:

Is there any challenge / (Kaggle) competition, where the state of the art are Bayesian Networks or at least very similar models?

(Side note: I've also seen decision trees, 2, 3, 4, 5, 6, 7 win in several recent Kaggle challenges)
One of the areas where Bayesian approaches are often used, is where one needs interpretability of the prediction system. You don't want to give doctors a Neural net and say that it's 95% accurate. You rather want to explain the assumptions your method makes, as well as the decision process the method uses. 

Similar area is when you have a strong prior domain knowledge and want to use it in the system.