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 ...

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...

evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. in this paper, bayesian belief networks (bbns) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. as a case study, we estimated the surface water evaporation of the persian gulf and worked with a dataset of observations (dai...

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...

This paper presents a methodology for real-time estimation of water distribution system state parameters using a dynamic Bayesian network to combine current observations with knowledge of past system behavior. The dynamic Bayesian network presented here allows the flexibility to model both discrete and continuous variables and represent causal relationships that exist within the distribution sy...