A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...

Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of ...

We have a situation when couple of variables needs to have different value for different targets. Currently we solved the issue in a way that we define variable with default empty string in variable ...

Modelled as Gaussian random variables, these errors lead to progressive misalignments that inevitably lead to collapse," explains the researcher. This problem goes beyond the simple child's game of ...

Problem description. (a) Random stacking process (b) Description of block positions (c) Illustration of the position of the centre of gravity of the upper part of the tower above level 3 (𝑛 = 6, 𝑖 = ...

Title: Reduced-Order Moment Closure Models for Uncertainty Quantification and Data Assimilation Abstract: We present a new strategy for the statistical forecasts of multiscale nonlinear systems ...

Abstract: The rapid expansion of wind power generation demands an update to the computational tools used for decision-making in the operation and planning of electrical power systems. This paper ...

This example code demonstrates the unfair scheduling of threads when using a condition variable. However after signalling the condition variable, the thread immediately re-acquires the resource.