Bayesian network (BN) modeling is a rapidly advancing field. Here we explore new methods
by which BN model development and application are being joined with other tools and …

Context: In scenarios where data availability issues hinder the applications of statistical
causal modeling in software engineering (SE), Bayesian networks (BNs) have been widely …

Background Frailty is a syndrome that is defined as an accumulation of deficits in physical,
psychological, and social domains. On a global scale, there is an urgent need to create …

A more scientific, objective, and reasonable renewal orientation is gradually becoming a
research hotspot in the field of industrial heritage conservation and renewal. This study …

Introduction Early warning scores (EWS) are being increasingly embedded in hospitals over
the world due to their promise to reduce adverse events and improve the outcomes of …

Latent variables (LVs), representing the unobservable abstract concepts, such as patient
disease and customer credit, play an important role in the simplification of network structure …

The characterisation of uncertainty is required for pragmatic decision making when sensor
data and other forms of information from several sources are fused in decision support …

Abstract Knowledge Acquisition (KA) methods are of paramount importance in the design of
intelligent systems. Research is ongoing to improve their effectiveness and efficiency …

Abstract The South African sunflower industry is considering transferring to a quality-based
marketing system driven by an incentive. However, the ability of silos to offer necessary …

Abstract In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning
and inference. However, Markov equivalence class considerations mean it is not always …