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Where are Bayesian regularized Neural Networks better over the usual NNs?
Can you give me an example of a real-world problem where Bayesian regularized Neural Networks have outperformed the usual NNs?
One example where Bayesian Neural Networks have shown superiority over traditional Neural Networks is in the field of medical diagnosis. Medical data is often noisy, incomplete, and highly variable, making it difficult to get accurate predictions. Bayesian Neural Networks have been used to predict the outcome of medical treatments, identify patients at high risk of particular diseases, and detect fraud in medical insurance claims. By incorporating the uncertainty in the model's parameters, Bayesian Neural Networks are able to make more accurate and informative predictions in these complex and dynamic domains.