Modeling & predictive method of Bayesian neural network

With neural networks,the main difficulty in model building is controlling the complexity of the model and lack of tools for analyzing the results,such as confidence interval.However,Bayesian approach can handle such problems by defining vague priors for the hyperparameters that determine the model complexity.And the Bayesian analysis yields posterior predictive distributions for any variables of interest,making the computation of confidence intervals possible.Prior knowledge about the model parameters can be incorporated in Bayesian inference and combined with training data to control complexity of different parts of the model.Markov chain Monte Carlo method is applied to optimize the model control parameters and obtain the predictive distribution.The Bayesian neural network method is studied and used in the drift modeling for gyroscopes.Results show that the Bayesian neural network can produce better modeling and predictive performance.