In this paper, we propose an analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks. The method enables the analytical Gaussian inference of the posterior mean vector and diagonal covariance matrix for weights and biases. The method proposed has a computational complexity of $\mathcal{O}(n)$ with respect to the number of parameters $n$, and the tests performed on regression and classification benchmarks confirm that, for a same network architecture, it matches the performance of existing methods relying on gradient backpropagation.

翻译：在本文中,我们提出了一种分析方法,用于在贝叶西亚神经网络中进行可移植近似高斯推理(TAGI),该方法使高斯人能够对后向媒介和对角变异矩阵的重量和偏差进行分析推理,就参数数而言,拟议方法的计算复杂性为$\mathcal{O}(n)美元,对回归和分类基准进行的测试证实,对于同一网络结构而言,该方法与依赖梯度反向回向调整的现有方法的性能相符。