In this descriptive scoping review, we assessed how Bayesian network structures are built and learned in applied work by screening 5993 recent papers (2020–2025) whose abstracts mention “Bayesian (belief) network” and deeming 3661 relevant. Among these relevant papers, expert knowledge was used in 2059 papers (56.2%): 1785 (48.8%) relied on expert knowledge alone, whereas 274 (7.5%) combined expert input with algorithmic structure learning. Automatic structure learning thus remains underused. Data sharing was scarce: only 129 studies (3.5%) provided functional dataset links, which we curated into an open benchmark index. Among 1106 papers (30.2%) using algorithms without expert knowledge, score-based methods were most common (797, 72.1%; mainly Hill climbing, K2, and tabu), followed by constraint-based methods (194, 17.5%; mainly PC and Grow–Shrink), fixed- or restricted-topology BN classifiers (143, 12.9%; mainly TAN and naive Bayes), and hybrid methods (131, 11.8%; mainly MMHC); bootstrapping appeared in 223 papers (6.1%). Reported practice thus remains concentrated around familiar algorithms.
Task decomposition is a fundamental challenge in multi-agent collaborative maintenance systems, where unstructured natural language instructions must be precisely translated into logically coherent, executable sub-task sequences. This paper formulates task decomposition as a cons…
Machine learning technologies are increasingly embedded in financial decision-making processes, yet their influence on investor behavior and market efficiency remains insufficiently understood. This study investigates how machine learning-assisted investment decisions affect inve…
With the rapid advancement of artificial intelligence technologies in the era of big data, time series forecasting has become indispensable in critical fields such as environmental monitoring and financial market analysis. However, the existing forecasting models often encounter…
This paper develops a semi-closed-form pricing framework for vulnerable geometric Asian options under a three-factor stochastic volatility jump-diffusion model with stochastic interest rates. To the best of our knowledge, this is the new framework to simultaneously accommodate co…
Combinatorial optimization is a key component in critical decision problems such as routing, scheduling, network design, and graph optimization. Although combinatorial optimization methods, including exact algorithms, approximation methods, constraint programming, mixed integer p…
Longitudinal data, characterized by repeated measurements on the same subjects over time, are ubiquitous in biomedical sciences, economics, social sciences, and engineering. Analyzing such data presents unique statistical and computational challenges, including within-subject cor…