| Abstract: | In the field of Artificial Intelligence, Bayesian Networks (BN) are a well-known framework for reasoning under uncertain knowledge. BN have been applied in a wide range of real-world domains, such as medical diagnosis, forensic analysis, dependability assessment, risk management, etc. With respect to other types of models, BN provide relevant advantages: at the modelling level, the compact representation of the joint distribution of the system variables leads to the factorization of the set of possible states, avoiding the generation of the complete state space of the system; at the analysis level, inference algorithms can compute the probability distribution of any variable, possibly conditioned on the observation of the value (state) of other variables, so that predictive and diagnostic measures can be easily evaluated. During the years, BN have been extended in order to increase their modelling and analysis power; for instance, Dynamic Bayesian Networks and Continuous-Time Bayesian Networks take time into account, Hybrid Bayesian Networks deal with both discrete and continuous variables, Decision Networks contain decision nodes and value nodes. The aim of this Special Issue is to collect recent developments about inference algorithms, their applications to real-case studies, and their implementation in software tools. |
