The continuous growth of data has created a demand for better data fusion algorithms. In this study we have used a method called Bayesian networks to answer the demand. The reason why Bayesian networks are used in wide range of applications is that modelling with Bayesian networks offers easy and straightforward representation for combining a priori knowledge with the observations. Another reas...

Abstract This paper presents a new class of Bayesian networks called hybrid semiparametric networks, which can model data (discrete and continuous data) by mixing parametric nonparametric estimation models. The models represent conditional linear Gaussian relationship between variables, while the other types relationships, such as non-Gaussian nonlinear relationships. generalizes including them...

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences su...

A Bayesian network can be regarded as a summary of a domain expert’s experience with an implicit population. A database can be regarded as a detailed documentation of such an experience with an explicit population. This connection between Bayesian networks and databases is well recognized and have been pursued for knowledge acquisition [1, 2, 11]. Existing databases are treated as information r...

A convenient way of modelling complex interactions is by employing graphs or networks which correspond to conditional independence structures in an underlying statistical model. One main class of models in this regard are Bayesian networks, which have the drawback of making parametric assumptions. Bayesian nonparametric mixture models offer a possibility to overcome this limitation, but have ha...

Probabilistic graphical models are useful tools for modeling systems governed by probabilistic structure. Bayesian networks are one class of probabilistic graphical model that have proven useful for characterizing both formal systems and for reasoning with those systems. Probabilistic dependencies in Bayesian networks are graphically expressed in terms of directed links from parents to their ch...