论文标题
贝叶斯调整高斯：有条件的高斯先验，用于稳定的多旋翼估计
Where Bayes tweaks Gauss: Conditionally Gaussian priors for stable multi-dipole estimation
论文作者
论文摘要
我们提出了对先前描述的模型和算法的非常简单而强大的概括，用于估算磁电/电脑电图数据的多个偶极子。具体而言，概括在于在一组有条件的线性/高斯变量的标准偏差上引入对数均匀的高位。我们使用数值模拟和实验数据集来表明，在高参数的广泛值下，与后验分布的近似值保持极为稳定，实际上消除了对高参数的依赖性。
We present a very simple yet powerful generalization of a previously described model and algorithm for estimation of multiple dipoles from magneto/electro-encephalographic data. Specifically, the generalization consists in the introduction of a log-uniform hyperprior on the standard deviation of a set of conditionally linear/Gaussian variables. We use numerical simulations and an experimental dataset to show that the approximation to the posterior distribution remains extremely stable under a wide range of values of the hyperparameter, virtually removing the dependence on the hyperparameter.
