In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete data—that is, in the presence of missing values or hidden variables. In a recent paper, I introduced an algorithm called Structural EM that combines the standard Expectation Maximization (EM)...

There is a critical need for data-mining methods that can identify SNPs that predict among individual variation in a phenotype of interest and reverse-engineer the biological network of relationships between SNPs, phenotypes, and other factors. This problem is both challenging and important in light of the large number of SNPs in many genes of interest and across the human genome. A potentially...

We present a new approach for combining the beliefs of many individuals using graphical models. Existing Bayesian belief aggregation methods break several theoretical assumptions for Bayesian reasoning. More practically, existing opinion pool functions that compute a single value to represent the belief of all contributors do not represent reality well, especially in cases where there are many ...

The problem of profiling and filtering the Web content is important particularly for mobile applications where wireless network traffic and mobile terminal size are limited comparing to the Internet access from the PC. Bayesian networks are known to be good tool for learning user preferences in electronic commerce. However more sophisticated cases, when user preferences are changing according t...

Een wetenschappelijke proeve op het gebied van de Natuurwetenschappen, Wiskunde en Informatica Proefschrift ter verkrijging van de graad van doctor aan de Radboud Universiteit Nijmegen, op gezag van de rector magnificus prof. mr. Contents Title page i Table of Contents iii 1 Introduction 1 1.1 A gentle introduction to graphical models 1 1.1.1 The Asia network: an example of a Bayesian network 2...

This work is a technical approach to modeling false information nature, design, belief impact and containment in multi-agent networks. We present a Bayesian mathematical model for source information and viewer’s belief, and how the former impacts the latter in a media (network) of broadcasters and viewers. Given the proposed model, we study how a particular information (true or false) can be op...

Ventricular extrasystoles (VE) are considered the most dangerous type of heart rhythm disorders for human life, their timely detection, diagnosis and prevention urgent issues cardiology. In order to ensure objectivity VE, it is necessary process a large amount information related results various medical studies, tests, anamnesis, accompanying diseases, etc., along with long-term Holter ECG moni...