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Dive into the research topics where Martijn A. R. Leisink is active.

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Featured researches published by Martijn A. R. Leisink.


BMC Bioinformatics | 2006

The Cluster Variation Method for Efficient Linkage Analysis on Extended Pedigrees

Cornelis A. Albers; Martijn A. R. Leisink; Hilbert J. Kappen

BackgroundComputing exact multipoint LOD scores for extended pedigrees rapidly becomes infeasible as the number of markers and untyped individuals increase. When markers are excluded from the computation, significant power may be lost. Therefore accurate approximate methods which take into account all markers are desirable.MethodsWe present a novel method for efficient estimation of LOD scores on extended pedigrees. Our approach is based on the Cluster Variation Method, which deterministically estimates likelihoods by performing exact computations on tractable subsets of variables (clusters) of a Bayesian network. First a distribution over inheritances on the marker loci is approximated with the Cluster Variation Method. Then this distribution is used to estimate the LOD score for each location of the trait locus.ResultsFirst we demonstrate that significant power may be lost if markers are ignored in the multi-point analysis. On a set of pedigrees where exact computation is possible we compare the estimates of the LOD scores obtained with our method to the exact LOD scores. Secondly, we compare our method to a state of the art MCMC sampler. When both methods are given equal computation time, our method is more efficient. Finally, we show that CVM scales to large problem instances.ConclusionWe conclude that the Cluster Variation Method is as accurate as MCMC and generally is more efficient. Our method is a promising alternative to approaches based on MCMC sampling.


artificial intelligence in medicine in europe | 2007

Inference in the Promedas Medical Expert System

Bastian Wemmenhove; Joris M. Mooij; Wim Wiegerinck; Martijn A. R. Leisink; Hilbert J. Kappen; Jan P. Neijt

In the current paper, the Promedas model for internal medicine, developed by our team, is introduced. The model is based on up-to-date medical knowledge and consists of approximately 2000 diagnoses, 1000 findings and 8600 connections between diagnoses and findings, covering a large part of internal medicine. We show that Belief Propagation (BP) can be successfully applied as approximate inference algorithm in the Promedas network. In some cases, however, we find errors that are too large for this application. We apply a recently developed method that improves the BP results by means of a loop expansion scheme. This method, termed Loop Corrected (LC) BP, is able to improve the marginal probabilities significantly, leaving a remaining error which is acceptable for the purpose of medical diagnosis.


neural information processing systems | 2000

A Tighter Bound for Graphical Models

Martijn A. R. Leisink; Hilbert J. Kappen

We present a method to bound the partition function of a Boltzmann machine neural network with any odd-order polynomial. This is a direct extension of the mean-field bound, which is first order. We show that the third-order bound is strictly better than mean field. Additionally, we derive a third-order bound for the likelihood of sigmoid belief networks. Numerical experiments indicate that an error reduction of a factor of two is easily reached in the region where expansion-based approximations are useful.


Neural Networks | 2000

Learning in higher order Boltzmann machines using linear response

Martijn A. R. Leisink; Hilbert J. Kappen

We introduce an efficient method for learning and inference in higher order Boltzmann machines. The method is based on mean field theory with the linear response correction. We compute the correlations using the exact and the approximated method for a fully connected third order network of ten neurons. In addition, we compare the results of the exact and approximate learning algorithm. Finally we use the presented method to solve the shifter problem. We conclude that the linear response approximation gives good results as long as the couplings are not too large.


international conference on artificial neural networks | 2002

Linkage Analysis: A Bayesian Approach

Martijn A. R. Leisink; Hilbert J. Kappen; Han G. Brunner

In this article we propose a method for linkage analysis that is based on Bayesian statistics. It is non-parametric in the sense that there is no need to specify, disease, parameters such as penetrance values. We show that the method has significantly more statistical power than existing methods on axtificially created databases. Finally, the possibility to extend the method to multi-locus diseases is discussed.


Journal of Artificial Intelligence Research archive | 2003

Bound propagation

Martijn A. R. Leisink; Bert Kappen


uncertainty in artificial intelligence | 2002

General Lower bounds based on computer generated higher order expansions

Martijn A. R. Leisink; Hilbert J. Kappen


uncertainty in artificial intelligence | 2002

Computer generated higher order expansions

Martijn A. R. Leisink; Hilbert J. Kappen


neural information processing systems | 2001

Means, Correlations and Bounds

Martijn A. R. Leisink; Bert Kappen


Lecture Notes in Computer Science | 2002

Linkage analysis: A Bayesian approach

Martijn A. R. Leisink; Hilbert J. Kappen; Han G. Brunner

Collaboration


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Hilbert J. Kappen

Radboud University Nijmegen

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Han G. Brunner

Radboud University Nijmegen

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Wim Wiegerinck

Radboud University Nijmegen

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Bert Kappen

Radboud University Nijmegen

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C.C.A.M. Gielen

Radboud University Nijmegen

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Tom Heskes

Radboud University Nijmegen

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Bastian Wemmenhove

Radboud University Nijmegen

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Bert B.A. de Vries

Radboud University Nijmegen

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Cornelis A. Albers

Radboud University Nijmegen

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