Ensuring sustainability in the global world today depends on perception management as well as financial management. In order to manage the perceptions, which are inherently latent variables as they are measured indirectly through their indicators, they must be accurately handled and modelled comprehensively. In the present study, a hybrid technique...
Grafi predstavljajo intuitiven način za vizualiziranje in razumevanje kompleksnih odnosov med različnimi slučajnimi spremenljivkami. Vozlišča grafa predstavljajo slučajne spremenljivke, manjkajoče povezave pa predstavljajo pogojne neodvisnosti med spremenljivkami. Pravila, ki prevedejo lastnosti grafa v stavke pogojne neodvisnosti med spremenljivka...
Predicting energy consumption has become a critical issue for energy-intensive industrial contexts. A significant contribution to their overall energy load is due to the Heating Ventilation and Air Conditioning (HVAC) systems. This work, therefore, aims to validate the applicability of a probabilistic graphical approach, the Bayesian Network, in pr...
In this paper, we provide an approach to learning optimal Bayesian network (BN) structures from incomplete data based on the BIC score function using a mixture model to handle miss- ing values. We have compared the proposed approach with other methods. Our experiments have been conducted on different models, some of them Belief Noisy-Or (BNO) ones....
Les véhicules aériens sans pilote (UAV) prospèrent dans des environnements difficiles, améliorant la qualité des missions, la productivité et la sécurité. Opérer dans des contextes imprévisibles nécessite une prise de décision indépendante en temps réel pour une gestion efficace des missions. Ce document se concentre sur les missions collaboratives...
Probabilistic inference in high-dimensional continuous or hybrid domains poses significantchallenges, commonly addressed through discretization, sampling, or reliance on parametricassumptions. The drawbacks of these methods are well-known: inaccuracy, slow computationalspeeds or overly constrained models.This paper introduces a novel general infere...
Constructing Bayesian networks (BN) for practical applications presents significant challenges, especially in domains with limited empirical data available. In such situations, field experts are often consulted to estimate the model’s parameters, for instance, rank correlations in Gaussian copula-based Bayesian networks (GCBN). Because there is no ...
Failure diagnosis and prognosis in industry is crucial to avoid unplanned outages, optimize efficiency and reduce maintenance costs. The use of Bayesian based networks allows an accurate and probabilistic evaluation, improving decision making and strategic maintenance planning. The objective of this research lies in the presentation of a detailed m...
Disruption risk assessment is a primary and crucial step before taking measures to mitigate the negative impact of disruptions propagating along supply chains (SCs). Recently, robust dynamic Bayesian network (DBN) provides a valid tool for disruption risk estimation under the ripple effect in a data-scarce environment. However, existing literature ...
Bayesian networks are graphical first-order probabilistic models that allow for a compact representation of large probability distributions, and for efficient inference, both exact and approximate. We introduce a higherorder programming language-in the idealized form of a-calculus-which we prove sound and complete w.r.t. Bayesian networks: each Bay...
