Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most real-world applications also contain continuous ones. In this paper we introduce hybrid MLNs, in which continuous properties (e.g., the distan...

Multiple hypothesis testing and clustering have been the subject of extensive research in high-dimensional inference, yet these problems usually have been treated separately. By defining true clusters in terms of shared parameter values, we could improve the sensitivity of individual tests, because more data bearing on the same parameter values are available. We develop and evaluate a hybrid me...

An algebraic approach is proposed to calculate stabilities in a colored graph with hybrid preference. The algebraic approach establishes a hybrid framework for stability analysis by combining strength of preference and unknown preference. The hybrid system is more general than existing models, which consider preference strength and preference uncertainty separately. Within the hybrid preference...

This study aims to quantify suction velocities for hybrid laminar flow control of a vertical fin by taking data and model uncertainties into account. Because cannot be measured directly, they are calculated using the Darcy–Forchheimer model, which needs pressure distribution porosity characteristics as input. Uncertainties in characterized novel fusion method incorporating wind-tunnel numerical...

Farmer adaptation to climatic variability is explored via an analysis of corn hybrid selection in southern Ontario. Corn hybrid varieties are available for a wide range of conditions, including accumulated heat, measured as Corn Heat Units (CHUS). Hybrids with higher CHU ratings generally have higher yields, so long as there is sufficient heat for them to mature. Farmers choose their hybrid var...