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Abstract:The Bayesian approach provides consistent way to do inference by integrating the evidence from data with prior knowledge from the problem. Bayesian neural networks can overcome the main difficulty of controlling the model's complexity in modelling building of standard neural network. And the Bayesian approach offers efficient tools for avoiding overfitting even with very complex models, and facilitates estimation of the confidence intervals of the results. In this paper, we review the Bayesian methods for neural networks. And then the structure of Bayesian neural networks is designed in this paper, and real detected drift data of a DTG is used to prove the effectiveness of the method. The results show the Bayesian neural networks methods possess better predictive precision.

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