The smoothing distribution of dynamic probit models with Gaussian state dynamics was recently proved to belong to the unified skew-normal family. Although this is computationally tractable in small-to-moderate settings, it may become computationally impractical in higher dimensions. In this work, adapting a recent more general class of expectation propagation (EP) algorithms, we derive an efficient EP routine to perform inference for such a distribution. We show that the proposed approximation leads to accuracy gains over available approximate algorithms in a financial illustration.

翻译：具有高斯状态动态特性的动态Probit模型的平滑分布近期被证明属于统一偏斜正态族。尽管该分布在中小规模场景下具有计算可行性，但在高维情形下可能变得计算上不可行。本研究通过适配近期提出的更通用的期望传播(EP)算法类，推导出针对此类分布进行推断的高效EP程序。在金融案例应用中，我们证明所提出的近似方法相较于现有近似算法可带来精度提升。