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Nijmegen, Netherlands

Wiegerinck W.,SNN Adaptive Intelligence | Kappen B.,Radboud University Nijmegen | Burgers W.,SNN Adaptive Intelligence
Studies in Computational Intelligence | Year: 2010

Bayesian networks are widely accepted as models for reasoning with uncertainty. In this chapter, we focus on models that are created using domain expertise only. After a short review of Bayesian network models and common Bayesian network modeling approaches, we will discuss in more detail three applications of Bayesian networks.With these applications, we aim to illustrate the modeling power and flexibility of the Bayesian networks, which go beyond the standard textbook applications. The first network is applied in a system for medical diagnostic decision support. A distinguishing feature of this network is the large amount of variables in the model. The second one involves an application for petrophysical decision support to determine the mineral content of a well, based on borehole measurements. This model differs from standard Bayesian networks in terms of its continuous variables and nonlinear relations. Finally, we will discuss an application for victim identification by kinship analysis based on DNA profiles. The distinguishing feature in this application is that Bayesian networks are generated and computed on-the-fly based on case information. © 2010 Springer-Verlag Berlin Heidelberg. Source


Burgers W.,SNN Adaptive Intelligence | Wiegerinck W.,SNN Adaptive Intelligence | Wiegerinck W.,Radboud University Nijmegen | Kappen B.,Radboud University Nijmegen | Spalburg M.,Royal Dutch Shell
Expert Systems with Applications | Year: 2010

The exploration for oil and gas requires real-time petrophysical expertise to interpret measurement data acquired in boreholes and to recommend further steps. High time pressure and the far reaching nature of these decisions, as well as the limited opportunity to gain in depth petrophysical experience suggests that a decision support system that can aid the petrophysicist will be very useful. In this paper we describe a Bayesian approach for obtaining compositional estimates that combines expert knowledge with information obtained from measurements. We define a prior model for the compositional volume fractions and observation models for each of the measurement tools. Both prior and observation models are based on domain expertise. These models are combined in a joint probability model. To deal with the nonlinearities in the model, Bayesian inference is implemented by using the hybrid Monte Carlo algorithm. In the system, tool measurement values can entered and the posterior probability distribution of the compositional fractions can be obtained by applying Bayes' rule. Bayesian inference is also used for optimal tool selection, using conditional entropy to select the most informative tool to obtain better estimates of the reservoir. Reliability and consistency of the method is demonstrated by inference on synthetically generated data. © 2010 Elsevier Ltd. All rights reserved. Source


Van Dongen C.J.,Netherlands Forensic Institute | Slooten K.,Netherlands Forensic Institute | Slagter M.,Netherlands Forensic Institute | Burgers W.,SNN Adaptive Intelligence | Wiegerinck W.,SNN Adaptive Intelligence
Forensic Science International: Genetics Supplement Series | Year: 2011

The Netherlands Forensic Institute (NFI), together with SNN at Radboud University Nijmegen, have developed new software for pedigree matching which can handle autosomal, Y chromosomal and mitochondrial DNA profiles. Initially this software, called Bonaparte, has been developed for DNA DVI. Bonaparte has been successfully applied in a real DVI case: the Afriqiyah Airways crash in Tripoli, Libya on 12 May 2010 in which 103 persons perished. The software performed excellently in terms of computational performance, stability and user-friendliness. This showed that Bonaparte is a reliable and time-saving tool which significantly simplifies and enhances a large-scale victim identification process. Bonaparte has been applied in NFIs missing persons program. For this, the software is connected to the NFI's missing persons database (CODIS). Since Bonaparte uses XML as import format, data from any source can be imported. In the new configuration, CODIS data is automatically imported into Bonaparte. Then the software automatically performs a set of direct searches, as well as searches against both partial and full family trees. For the autosomal DNA results, exact likelihood ratios are computed. Finally, match reports can be generated on demand by Bonaparte's customized reporting modules. In this way, an advanced search strategy combined with a modern, efficient work flow is realized in NFI's missing persons program. © 2011 Elsevier Ireland Ltd. Source


Burgers W.G.,SNN Adaptive Intelligence | Wiegerinck W.A.J.J.,SNN Adaptive Intelligence | Kappen H.J.,SNN Adaptive Intelligence | Spalburg M.R.,Royal Dutch Shell
Belgian/Netherlands Artificial Intelligence Conference | Year: 2010

In this paper we describe a Bayesian approach for obtaining compositional estimates that combines expert knowledge with information obtained from measurements. We define a prior model for the compositional volume fractions and observation models for each of the measurement tools. Both prior and observation models are based on domain expertise. These models are combined in a joint probability model. To deal with the nonlinearities in the model, Bayesian inference is implemented by using the hybrid Monte Carlo algorithm. Source

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