With maximizing the marginal likelihood function of hyper-parameters, the optimal weights are acquired, i. e. the reconstructed image.

最大化超参数的边缘对数似然函数求得权值参数的最优估计即待重建图像。

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In Bayesian reference, marginal likelihood function involve to compute high dimensional complex integrand. So exactly to compute marginal likelihood is often difficult.

贝叶斯推断中边际似然函数涉及到维数较高的复杂积分的计算，因而精确地计算边际似然函数往往有困难。

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