Bayesian inference in neural networks
神经网络中的贝叶斯推断
Approximate marginal Bayesian computation and inference are developed for neural network models. The marginal considerations include determination of approximate Bayes factors for model choice about the number of nonlinear sigmoid terms, approximate predictive density computation for a future observable and determination of approximate Bayes estimates for the nonlinear regression function. Standard conjugate analysis applied to the linear parameters leads to an explicit…