Abstract Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially causal discovery algorithms, yet it remains highly challenging problem due to dimensionality complex relationships presented data. In this study, we introduce LCIT (Latent representation-based Conditional Independence Test)—a novel method for independence testing based on...

A key to knowledge graph embedding (KGE) is choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose unified perspective of introduce uncertainty into KGE from the view group theory. Our model can incorporate existing models (i.e., generality), ensure computation tractable efficiency) enjoy expressive power random variables expre...

Stokes inversion techniques are very powerful methods for obtaining information on the thermodynamic and magnetic properties of solar stellar atmospheres. In recent years, highly sophisticated codes have been developed that now routinely applied to spectro-polarimetric observations. Most these designed find an optimum solution nonlinear inverse problem. However, obtain location potentially mult...