In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data con­ taining both continuous and discrete vari­ ables. We describe a new technique for multivariate discretization, whereby each continuous variable is discretized while tak­ ing into account its interaction with the other variables. The technique is based on the use of a Bayesian...

Flight delay creates major problems in the current aviation system. Methods are needed to analyze the manner in which micro-level causes propagate to create system-level patterns of delay. Traditional statistical methods are inadequate to the task. This paper proposes the use of Bayesian networks (BNs) to investigate and visualize propagation of delays among airports. The BN structure was devel...

Automatic generation of Bayesian network (BNs) structures (directed acyclic graphs) is an important step in experimental study of algorithms for inference in BNs and algorithms for learning BNs from data. Previously known simulation algorithms do not guarantee connectedness of generated structures or even successful genearation according to a user speciication. We propose a simple, eecient and ...

Medicina Balear 2014; 29 (3): 10-17 Bayesian networks (BNs)16, 25 also referred to as causal networks or beliefs networks, are a form of statistical modelling which allow us to obtain a graphical network describing the dependencies and conditional independencies from empirical data. They have proven to be a promising tool for discovering relationships9, they capture the way an expert understand...

The main goal of this paper is to describe inference in hybrid Bayesian networks (BNs) using mixture of polynomials (MOP) approximations of probability density functions (PDFs). Hybrid BNs contain a mix of discrete, continuous, and conditionally deterministic random variables. The conditionals for continuous variables are typically described by conditional PDFs. A major hurdle in making inferen...

Designing a large Bayesian network (BN) has been regarded as a diicult process. It has been suggested that BN libraries can be used to facilitate the construction of a large BN. That is, a large BN can be deened in terms of smaller BNs stored in a library. In this paper, we point out that it may be possible to combine the conditional independen-cies deened by the smaller BNs, but not the smalle...

Flight delay creates major problems in the current aviation system. Methods are needed to analyze the manner in which micro-level causes propagate to create system-level patterns of delay. Traditional statistical methods are inadequate to the task. This paper proposes the use of Bayesian networks (BNs) to investigate and visualize propagation of delays among airports. The BN structure was devel...

Several researchers have suggested that Bayesian networks (BNs) should be used to manage the inherent uncertainty in information retrieval. However, it has been argued that manually constructing a large BN is a difficult process. In this paper, we obtain the only minimal complete subset of the semi-graphoid axiomatization governing the independency information in a BN. This result may be useful...

agents are designed to utilize the known methods of machine learning with Bayesian Networks (BN): parameter learning and structure learning. In addition, a new method of machine learning with BNs, termed utility learning in this paper, is introduced. BN software for Matlab is used to realize the proposed agent. Additional software is written to simulate the PRT problem using various intelligent...

It is a well-known fact that the Bayesian Networks’ (BNs) use as classifiers in different fields of application has recently witnessed a noticeable growth. Yet, the Naïve Bayes’ application, and even the augmented Naïve Bayes’, to classifier-structure learning, has been vulnerable to certain limits, which explains the practitioners’ resort to other more sophisticated types of algorithms. Conseq...